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People generally understand text better if the text is in their native language. Because of an increased use of personality assessment in the selection of employees and students, it is important to provide questionnaires in people's local languages. In this study, we developed the 120-item International Personality Item Pool (IPIP) Urdu version using the Darwish translation model. The translation was verified by a panel of engineering experts and Urdu and English language experts. Moreover, an empirical investigation was conducted to determine the internal consistency, reliability and construct validity of the Urdu version. The Urdu version was internally consistent, more reliable and had higher convergent validity than its corresponding English scale., Author(s): Iftikhar Ahmed Khan [sup.1], Ahmad Khan [sup.2], Babar Nazir [sup.1], Syed Sajid Hussain [sup.1], Fiaz Gul Khan [sup.1], Imran Ali Khan [sup.1] Author Affiliations: (1) grid.418920.6, 0000 0004 0607 [...]

In this study, we grew pristine and Ni-doped vertically aligned zinc oxide nanowires (NWs) on a glass substrate. Both the doped and pristine NWs displayed dominant 002 peaks, confirming their vertical alignment. The Ni-doped NWs exhibited a leftward shift compared to the pristine NWs. TEM measurements confirmed the high crystallinity of individual NWs, with a d-spacing of ~0.267 nm along the c-axis. Ni-doped NWs had a higher density, indicating increased nucleation sites due to nickel doping. Doped NW films on glass showed enhanced absorbance in the visible region, suggesting the creation of sub-gap defect levels from nickel doping. Magnetization vs. magnetic field measurements revealed a small hysteresis loop, indicative of soft ferromagnetic behavior. Current transient plots demonstrated an increase in current with an applied magnetic field. Two-terminal devices exhibited a photo response that intensified with magnetic field application. This increase was attributed to parallel grain alignment, resulting in enhanced carrier concentration and photo response. In the dark, transport properties displayed negative magnetoresistance behavior. This magneto-transport effect and enhanced photo response (under an LED at ~395 nm) were attributed to giant magnetoresistance (GMR) in the aligned NWs. The observed behavior arose from reduced carrier scattering, improved transport properties, and parallel spin alignment in the magnetic field. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2010.04.004 Byline: Fiaz Gul Khan (b), Kalim Qureshi (a), Babar Nazir (b) Keywords: Grid computing; Fault tolerance; Resource management; Distributed system Abstract: As fault tolerance is the ability of a system to perform its function correctly even in the presence of faults. Therefore, different fault tolerance techniques (FTTs) are critical for improving the efficient utilization of expensive resources in high performance grid computing systems, and an important component of grid workflow management system. This paper presents a performance evaluation of most commonly used FTTs in grid computing system. In this study, we considered different system centric parameters, such as throughput, turnaround time, waiting time and network delay for the evaluation of these FTTs. For comprehensive evaluation we setup various conditions in which we vary the average percentage of faults in a system, along with different workloads in order to find out the behavior of FTTs under these conditions. The empirical evaluation shows that the workflow level alternative task techniques have performance priority on task level checkpointing techniques. This comparative study will help to grid computing researchers in order to understand the behavior and performance of different FTTs in detail. Author Affiliation: (a) Information Science Department Kuwait University, Kuwait (b) Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad, Pakistan Article History: Received 9 January 2009; Revised 3 February 2010; Accepted 29 April 2010 Article Note: (footnote) [star] Reviews processed and proposed for publication to the Editor-in-Chief by Associate Editor Dr. Sahin.

The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there is no such policy of cloud SLA monitoring to minimize SLA violations. In this work, we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts, for critical and non-critical parameters. The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation. This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach (RPAA) which will monitor SLA at runtime, analyze the SLA parameters and try to find the possibility of SLA violations. We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection. We have defined two main components of SLA-PRAA i.e., (a) Handler and (b) Accounting and Billing Manager. We have also described the function of both components through algorithms. The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies. [ABSTRACT FROM AUTHOR]

Computational grid is a pool of computational resources that is used by various organizations for storage purposes and execution of a large number of tasks. Load balancing and energy consumption are two core performance parameters in the computational grid. Since, performance of a computational grid is highly dependable to variations in load and energy consumption, therefore, it is important to pay attention to these aspects in grid. Therefore, we proposed an energy‐aware dynamic load balancing strategy for tasks scheduling in grid referred to as dynamic energy efficient load balancing strategy (DEEL) for computational grid. DEEL combined energy efficiency and load balancing paradigms in order to improve the utilization and reduce the energy consumption of resources. Simulations were performed by using GridSim. Simulation results were compared with published strategy IEGDC with respect to performance indices: finished gridlets, unfinished gridlets, resubmitted times, and response time. The simulation results were evaluated through performance metrics that is, (a) cost, (b) energy consumption, and (c) energy saved. As per simulation results, DEEL outperformed the published method IEGDC by reducing the execution time of finished gridlets up to 28%, minimizing the execution and resource cost up to 35%, and saving the energy consumption up to 38%. [ABSTRACT FROM AUTHOR]

Abstract We present a sleep/wake schedule protocol for minimizing end-to-end delay for event driven multi-hop wireless sensor networks. In contrast to generic sleep/wake scheduling schemes, our proposed algorithm performs scheduling that is dependent on traffic loads. Nodes adapt their sleep/wake schedule based on traffic loads in response to three important factors, (a) the distance of the node from the sink node, (b) the importance of the node's location from connectivity's perspective, and (c) if the node is in the proximity where an event occurs. Using these heuristics, the proposed scheme reduces end-to-end delay and maximizes the throughput by minimizing the congestion at nodes having heavy traffic load. Simulations are carried out to evaluate the performance of the proposed protocol, by comparing its performance with S-MAC and Anycast protocols. Simulation results demonstrate that the proposed protocol has significantly reduced the end-to-end delay, as well as has improved the other QoS parameters, like average energy per packet, average delay, packet loss ratio, throughput, and coverage lifetime.

Summary: Cloud computing provides solutions to many scientific and business applications. Large‐scale scientific applications, which are structured as scientific workflows, are evaluated through cloud computing. In this paper, we proposed a Quality‐of‐Service‐aware fault‐tolerant workflow management system (QFWMS) for scientific workflows in cloud computing. We have considered two real‐time scientific workflows, i.e., Montage and CyberShake, for an evaluation of the proposed QFWMS. The results of the proposed QFWMS scheduling were evaluated through simulation environment WorkflowSim and compared with three well‐known heuristic scheduling policies: (a) minimum completion time (MCT), (b) Maximum‐minimum (Max‐min), and (c) Minimum‐minimum (Min‐min). By considering Montage scientific workflow, the proposed QFWMS reduces the make‐span 8.86%, 8.94%, and 5.53% compared with existing three heuristic policies. Similarly, the proposed QFWMS reduces the cost 6.19%, 3.52%, and 3.60% compared with existing three heuristic policies. Likewise, by considering CyberShake scientific workflow, the proposed QFWMS reduces the make‐span 19.54%, 21.41%, and 25.71% compared with existing three heuristic policies. Similarly, the proposed QFWMS reduces the cost 8.78%, 8.40%, and 8.61% compared with existing three heuristic policies. More so, for QFWMS, SLA is neither violated for time constraints nor for cost constraints. While for MCT, Max‐min and Min‐min scheduling policies, SLA is violated 32, 37, and 23 times, respectively. Conclusively, the proposed QFWMS scheduling and management system is one of the significant workflow management systems for execution and management of scientific workflows in cloud computing. [ABSTRACT FROM AUTHOR]

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task. [ABSTRACT FROM AUTHOR]