Optimal Placement Algorithms for Virtual Machines

Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers

Modified SPLICE and its Extension to Non-Stereo Data for Noise Robust Speech Recognition

In this paper, a modification to the training process of the popular SPLICE algorithm has been proposed for noise robust speech recognition. The modification is based on feature correlations, and enables this stereo-based algorithm to improve the performance in all noise conditions, especially in unseen cases. Further, the modified framework is extended to work for non-stereo datasets where clean and noisy training utterances, but not stereo counterparts, are required. Finally, an MLLR-based computationally efficient run-time noise adaptation method in SPLICE framework has been proposed. The modified SPLICE shows 8.6% absolute improvement over SPLICE in Test C of Aurora-2 database, and 2.93% overall. Non-stereo method shows 10.37% and 6.93% absolute improvements over Aurora-2 and Aurora-4 baseline models respectively. Run-time adaptation shows 9.89% absolute improvement in modified framework as compared to SPLICE for Test C, and 4.96% overall w.r.t. standard MLLR adaptation on HMMs.Comment: Submitted to Automatic Speech Recognition and Understanding (ASRU) 2013 Worksho

Evidence that cleavage of the precursor enzyme by autocatalysis caused secretion of multiple amylases by Aspergillus niger

AbstractThe observation that a mutant strain of Aspergillus niger isolated for protease overproduction accumulated Taka-amylase supported an earlier report that processing of the precursor amylase by protease resulted in the secretion of multiple amylases. Studies using a mutant strain revealed that such processing was not due to aspergillopepsin but to autocatalysis by an inherent protease activity of the precursor and glucoamylase. Alignment of protease sequences with glucoamylase showed regions of consensus with serine carboxypeptidase of A. niger. Thus point mutations in this region due to ultraviolet radiation apparently caused the mutant to evolve with enhanced protease activity that degraded the precursor and accumulated Taka-amylase

Performance Improvement of Cloud Computing Data Centers Using Energy Efficient Task Scheduling Algorithms

Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better USAge of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved

EPIDEMIOLOGY OF LOWER LIMB INJURIES IN UNIVERSITY LEVEL FOOTBALL AND HOCKEY PLAYERS OF PUNJAB, INDIA

The aim of the study was to find out the effects of epidemiology of lower limb injuries in university level football and hockey players of Punjab aged between 18 to 25 years. The sampling of this study confined to a group of 129 hockey players and 147 football players, (total 276 players) belonging to the state of Punjab. A thorough review of literature was done to develop a questionnaire on the basis of demographic data, predisposing factors, training profiles and extent of injury. Since the questionnaire was originally in English and local language so players were interviewed personally. Mean, standard deviation and percentile were calculated. Statistical significance was set up at p value ≤0.05. This study illustrates that ankle & foot were most affected sites (34.0% football, 36.4% hockey), the most common injuries were sprains (59.2% football, 48.8% hockey), strains (25.9% football, 29.5% hockey) and extent of injury was commonly moderate (51.7% football, 56.6% hockey). This study might be helpful for the players as well as trainers, coaches to formulate effective and appropriate training protocols with minimal risk of injuries.  Article visualizations

A study on ground water quality of industrial area at Gajraula (U.P.), India

The present study aims to identify the ground water contamination problem in villages located in the close vicinity of Gajraula industrial area at Gajraula (U.P.), India. Ground water samples were collected from different villages at the depth of 40 and 120 feet from earth’s surface layer. Analytical techniques as described in the standard methods for examination of water and waste water were adopted for physico-chemical analysis of ground water samples and the results compared with the standards given by WHO and BIS guidelines for drinking water. Water quality index was calculated for quality standard of ground water for drinking purposes. The present investigation revealed that the water quality is moderately degraded due to high range of seven water quality parameters such as Temperature (18.33-32.36 0C), conductivity (925.45-1399.59 ?mho/cm), TDS (610.80-923.73 mgL-1), Alkalinity (260.17- 339.83 mgL-1), Ca-Hardness (129.68-181.17 mgL-1), Mg-Hardness (94.07-113.50 mgLÉ1) and COD (13.99-25.62 mgL-1). The water quality index (WQI) also indicated the all the water quality rating comes under the standard marginal values (45-64) i.e. water quality is frequently threatened or impaired and conditions usually depart from natural or desirable levels

On the thermodynamics of reconciling quantum and gravity

Is thermodynamics consistent with the quantum gravity reconciliation hypothesis [A. G. Cohen et al. Phys. Rev. Lett. 82, 4971 (1999)], which establishes holographic dark energy models? Here, we have attempted to address this issue in the affirmative by concentrating on the first law of thermodynamics