An Analysis of the HRD Mechanisms Employed by the Submersible Pump Manufactures in Coimbatore City

Human Resource Development (HRD) is a continuous process of enabling and ensuring the development of employees in a systematic and planned way. No organization can grow and survive in the present day environment without growth and development of its people. Developing the human resource by upgrading their skills, extending their knowledge and competencies would lead to organizational development. Human Resource is the most important and valuable resource of every organization. Dynamic people can build dynamic organizations. Efficient employees can contribute to the effectiveness of an organization. Competent and motivated employees can make things happen and enable an organization to achieve its goal. Therefore, organizations should continuously ensure that the dynamism, competence, motivation and effectiveness of the employee always remain high. The present study is an attempt to contribute to a better understanding of the HRD climate prevailing in submersible manufacturing organizations. The general climate, HRD Mechanisms and OCTAPAC culture are better in submersible pump organizations

Measuring and Understanding Throughput of Network Topologies

High throughput is of particular interest in data center and HPC networks. Although myriad network topologies have been proposed, a broad head-to-head comparison across topologies and across traffic patterns is absent, and the right way to compare worst-case throughput performance is a subtle problem. In this paper, we develop a framework to benchmark the throughput of network topologies, using a two-pronged approach. First, we study performance on a variety of synthetic and experimentally-measured traffic matrices (TMs). Second, we show how to measure worst-case throughput by generating a near-worst-case TM for any given topology. We apply the framework to study the performance of these TMs in a wide range of network topologies, revealing insights into the performance of topologies with scaling, robustness of performance across TMs, and the effect of scattered workload placement. Our evaluation code is freely available

Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering for Web Page Ranking

Web content mining retrieves the information from web in more structured forms. The page rank plays an essential part in web content mining process. Whenever user searches for any information on web, the relevant information is shown at top of list through page ranking. Many existing page ranking algorithms were developed and failed to rank the web pages in accurate manner through minimum time feeding. In direction to address the above mentioned issues, Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering (LSSPFS-SXGBC) Approach is introduced for page ranking based on user query. LSSPFS-SXGBC Approach has three processes for performing efficient web page ranking, namely preprocessing, feature selection and clustering. LSSPFS-SXGBC Approach in account of the numeral of operator request by way of an input. Lancaster Stemming Preprocessed Analysis is carried out in LSSPFS-SXGBC Approach for removing the noisy data from the input query. It eradicates the stem words, stop words and incomplete data for minimizing the time and space consumption. Sammon Projective Feature Selection Process is carried out in LSSPFS-SXGBC Approach to select the relevant features (i.e., keywords) based on user needs for efficient page ranking. Sammon Projection maps the high-dimensional space to lower dimensionality space to preserve the inter-point distance structure. After feature selection, Stochastic eXtreme Gradient Boost Page Rank Clustering process is carried out to cluster the similar keyword web pages based on their rank. Gradient Boost Page Rank Cluster is an ensemble of several weak clusters (i.e., X-means cluster). X-means cluster partitions the web pages into ‘x’ numeral of clusters where each reflection goes towards the cluster through adjacent mean value. For every weak cluster, selected features are considered as the training samples. Subsequently, all weak clusters are joined to form the strong cluster for attaining the webpage ranking results. By this way, an efficient page ranking is carried out through higher accurateness and minimum time consumption. The practical validation is carried out in LSSPFS-SXGBC Approach on factors such ranking accurateness, false positive rate, ranking time and space complexity with respect to numeral of user query

Neural network modeling of convection heat transfer coefficient for the casson nanofluid

This paper presents applications of Artificial Neural Network (ANN) to develop a mathematical model of magnetohydrodynamic (MHD) flow and heat transfer in a Casson nanofluid. The model equations are solved numerically by Runge-Kutta Fehlberg method with shooting technique. In the developing ANN model, the performance of the various configuration were compared with various types of errors such as Mean Square Error (MSE), Mean Absolute Error (MAE) and Sum Square Error (SSE). The best ANN configuration incorporated two hidden layers with twenty five neurons in each hidden layer was able to construct convective heat transfer coefficients with MSE, MAE and SSE of 0.006346, 0.009813 and 1.015423%, respectively, and had R² of 0.741516. A good co-relation has been obtained between the predicted results and the numerical values.Publisher's Versio

NMR Determination of an Incommensurate Helical Antiferromagnetic Structure in EuCo2As2

We report $^{153}$Eu, $^{75}$As and $^{59}$Co nuclear magnetic resonance (NMR) results on EuCo$_2$As$_2$ single crystal. Observations of $^{153}$Eu and $^{75}$As NMR spectra in zero magnetic field at 4.3 K below an antiferromagnetic (AFM) ordering temperature $T_{\rm N}$ = 45 K and its external magnetic field dependence clearly evidence an incommensurate helical AFM structure in EuCo$_2$As$_2$. Furthermore, based on $^{59}$Co NMR data in both the paramagnetic and the incommensurate AFM states, we have determined the model-independent value of the AFM propagation vector ${\bf k}$ = (0, 0, 0.73 $\pm$ 0.07)2$\pi$/$c$ where $c$ is the $c$ lattice parameter. Thus the incommensurate helical AFM state was characterized by only NMR data with model-independent analyses, showing NMR to be a unique tool for determination of the spin structure in incommensurate helical AFMs.Comment: 6 pages, 4 figures, accepted for publication in Phys.Rev.

Contemporary Approach for Technical Reckoning Code Smells Detection using Textual Analysis

Software Designers should be aware of address design smells that can evident as results of design and decision. In a software project, technical debt needs to be repaid habitually to avoid its accretion. Large technical debt significantly degrades the quality of the software system and affects the productivity of the development team. In tremendous cases, when the accumulated technical reckoning becomes so enormous that it cannot be paid off to any further extent the product has to be abandoned. In this paper, we bridge the gap analyzing to what coverage abstract information, extracted using textual analysis techniques, can be used to identify smells in source code. The proposed textual-based move toward for detecting smells in source code, fabricated as TACO (Textual Analysis for Code smell detection), has been instantiated for detecting the long parameter list smell and has been evaluated on three sampling Java open source projects. The results determined that TACO is able to indentified between 50% and 77% of the smell instances with a exactitude ranging between 63% and 67%. In addition, the results show that TACO identifies smells that are not recognized by approaches based on exclusively structural information