Factors influencing organizational commitment of banking sector employees

Organizational Commitment has been conceptualised & measured in different ways. This study is an attempt to identify the factors influencing organizational commitment of banking sector employees in Chennai. It is also important as suggestions can be given to the banking sector in order to bring an awareness of the commitment level of employees. Gaining awareness of commitment level and the respective influencing factor will help concentrate on increasing the commitment of employees. Using the measures developed by Mowday; Steers and Porter, the researchers have exploited Factor analysis by Principle Component Method to identify the factors influencing the organizational commitment of employees of PSBs and NPSBs

Online Bidding Behaviour And Loss Aversion In Cloud Computing Markets: An Experiment

The last few years have witnessed a rapid growth in commoditization and consumption of IT services particularly due to the growing acceptance of cloud computing services. This in turn has led to newer forms of pricing the cloud services such as dynamic pricing. Infact, spot pricing, a dynamic pricing scheme has become mainstream. Cloud consumers using these schemes need to place their bids inorder to procure computing instances. Most of extant research on cloud dynamic pricing focuses on resource allocation problems and bidding strategies. We identify the need to look at behavioural biases of bidders to bring in a holistic perspective to cloud dynamic pricing discussions. In this paper, we conduct an experiment to elicit the impact of a behavioural bias namely, loss aversion, on a cloud consumer’s bidding behaviour. We discuss the social implications of our result to cloud consumers and the economic implications for cloud providers

AN EFFICIENT APPROACH TO IMPLEMENT FEDERATED CLOUDS

Cloud computing is one of the trending technologies that provide boundless virtualized resources to the internet users as an important services through the internet, while providing the privacy and security. By using these cloud services, internet users get many parallel computing resources at low cost. It predicted that till 2016, revenues from the online business management spent $4 billion for data storage. Cloud is an open source platform structure, so it is having more chances to malicious attacks. Privacy, confidentiality, and security of stored data are primary security challenges in cloud computing. In cloud computing, ‘virtualization' is one of the techniques dividing memory into different blocks. In most of the existing systems there is only single authority in the system to provide the encrypted keys. To fill the few security issues, this paper proposed a novel authenticated trust security model for secure virtualization system to encrypt the files. The proposed security model achieves the following functions: 1) allotting the VSM(VM Security Monitor) model for each virtual machine; 2) providing secret keys to encrypt and decrypt information by symmetric encryption.The contribution is a proposed architecture that provides a workable security that a cloud service provider can offer to its consumers. Detailed analysis and architecture design presented to elaborate security model

Energy Efficient Operation of Three Phase Induction Motor using Delstar Converter for Machine Tools

DELSTAR converter is an electronic system to be interfaced with the existing STAR – DELTA starter for machine tools. Induction motor consumes more power while it is operated at DELTA mode for a long time under no load condition. The proposed system gives the solution for the above stated problem. When the load on the motor is less than 40% of full load, it switches the motor to operate in STAR mode to save energy. When the load increases beyond 40%, it automatically switches the motor to operate in DELTA mode. The starting regimen is not disturbed. The proposed converter is recommended for applications where load changes are not more than 120 times/hour. This can be used with any capacity motor by choosing appropriate current transformers and setting the current level using the potentiometer built in. The proposed converter is designed for 5HP induction motor and experimentally tested

Transient wall shear stress estimation in coronary bifurcations using convolutional neural networks

Background and Objective: Haemodynamic metrics, such as blood flow induced shear stresses at the inner vessel lumen, are associated with the development and progression of coronary artery disease. Understanding these metrics may therefore improve the assessment of an individual's coronary disease risk. However, the calculation of such luminal Wall Shear Stress (WSS) using traditional Computational Fluid Dynamics (CFD) methods is relatively slow and computationally expensive. As a result, CFD based haemodynamic computation is not suitable for integrated and large-scale use in clinical settings. Methods: In this work, deep learning techniques are proposed as an alternative method to CFD, whereby luminal WSS magnitude can be predicted in coronary bifurcations throughout the cardiac cycle based on the steady state solution (which takes <120 seconds to calculate including preprocessing), vessel geometry and additional global features. The deep learning model is trained on a dataset of 101 patient-specific and 2626 synthetic left main bifurcation models with 26 separate patient-specific cases used as the test set. Results: The model showed high fidelity predictions with <5% (normalised against mean WSS magnitude) deviation to CFD derived values as the gold-standard method, while being orders of magnitude faster with on average <2 minutes versus 3 hours computation for transient CFD. Conclusions: This method therefore offers a new approach to substantially reduce the computational cost involved in, for example, large-scale population studies of coronary haemodynamic metrics, and may therefore open the pathway for future clinical integration

Dimensionality Reduction Using Band Selection Technique for Kernel Based Hyperspectral Image Classification

AbstractHyperspectral images have abundant of information stored in the various spectral bands ranging from visible to infrared region in the electromagnetic spectrum. High data volume of these images have to be reduced, preserving the original information, to ensure efficient processing. In this paper, dimensionality reduction is done on Indian Pines and Salinas-A datasets using inter band block correlation coefficient technique followed by Singular Value Decomposition (SVD) and QR decomposition. The dimensionally reduced images are classified using GURLS and LibSVM. Classification accuracies of the original image is compared to that of the dimensionally reduced image. The experimental analysis shows that, for 10% training sample the overall accuracy, average accuracy and kappa coefficient of the dimensionally reduced image (about 50% of the dimension is reduced) is i)83.52%, 77.18%, 0.8110 for Indian Pines and ii)99.53%, 99.40%, 0.9941 for Salinas-A dataset which is comparable to that of original image i)84.67%, 82.28%, 0.8247 for Indian Pines and ii)99.32%, 99.18%, 0.9916 for Salinas-A dataset

SoC Estimation and Monitoring of Li-ion Cell using Kalman-Filter Algorithm

With the rise in an energy crisis, electric vehicles have become a necessity. An integral part of the electric/hybrid vehicle is batteries. Out of many types, Li-ion batteries are providing features like high power as well as energy density. The features make Li-ion is an excellent choice for multiple applications from electronic appliances to electric vehicles. Li-ion batteries have their limitations while using in electric vehicles, and battery parameter monitoring like temperature, voltage, current, State of Charge (SoC), etc. is very much essential. The monitoring is dependent on actual physical measurements, which are subject to error contributing factors such as measurement noise, errors etc. With the estimation of SOC and State of Health (SoH) of the battery model, the lifetime of the battery will be calculated out, and along these lines sparing significant cost. In this paper, a study on SoH estimation and Li-ion battery SoC is estimated using a Kalman Filter (KF) algorithm estimation and results are presented to validate the Li-ion operating performanc

Method Development and Validation of Fluconazole and Ivermectin in pure and Combined Dosage form using UV by Q- absorption ratio and Vierotd’s method

Multicomponent analysis involves simultaneous estimation of drug substances in combined dosage form. A comparative study between Q-Absorption ratio technique and Simultaneous Equation Method has been established for effective implication of the developed method. Numerous trials were performed as a part of solvent selection, both the drugs under study Fluconazole (FLN) and Ivermectin (IVR) have shown good solubility in methanol (Spectroscopic Grade). The absorption maxima (λmax) were found to be 261nm for FLU and 245nm for IVR respectively. Both the drugs were showing same extinction coefficient (Isosbestic point) at 261nm. A comparative study is proposed to be established for simultaneous equation and Q-absorption ratio method. Between the concentration and absorbance, calibration curves revealed a linear relationship. The regression line equation was established and identified r2 for Fluconazole and Ivermectin is 0.999 and 0.999. Fluconazole and Ivermectin were assessed by the validated method when each of the drug individually subjected to various stress conditions like concentrated acidic, basic, peroxide, excessive light and thermal. For estimation, the suggested approach was used for drug content in locally available marketed formulations which has proven successful for routine analysis of the same with the application of vierotd’s as well as Q-absorption method

Effectiveness of parent child interaction therapy on behavioral problems among the school age children.

“A study to assess the effectiveness of parent child interaction therapy on behavioral problems among school age children residing at Annanagar , Madurai”. The study was carried out to assess the behavioral problems before and after parent - child interaction therapy among school age children and to determine the effectiveness of parent – child interaction therapy on behavioral problems among school age children .The conceptual framework of the study is based on the Daniel stuffle beam’s programme evaluation model This study was conducted using one group pre test – post test pre experimental design. Convenient sampling technique was used to select Annanagar. The children who fulfill the inclusion criteria were selected by simple random sampling technique.The sample size was 40. The behavioral problems were assessed by modified Eyberg child behavior inventory. The tool was valid and the reliability was checked by split half technique and was found to be r = 0.8. The parent child interaction therapy was implemented for a period of one week. Data collection was done and the data obtained were analyzed in terms of both descriptive and inferential statistics. Findings of the study were the mean pre test and post test scores pertaining to arguing with parents about rules was the mean post test score (1.87) after parent - child interaction therapy was lesser than the mean pre test score (6.57). Pertaining to verbally fighting with sisters and brothers , the mean posttest score (2.07) after parent - child interaction therapy was lesser than the mean pre test score (4.82).The overall mean post test behavioral problems (68.9) after parent child interaction therapy was lesser than the mean pre test (177.92). There is a significant association between the demographic variables (ages, monthly income) and the post test mean score