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Articles by Kamaljit Kaur
Total Records ( 2 ) for Kamaljit Kaur
  Kamaljit Kaur , Rajesh K. Jalota and David J. Midmore
  Tree clearing is a topical issue the world over. In Queensland, the high rates of clearing in the past were mainly to increase pasture production. The present research evaluates the impact of clearing on some soil biological properties, i.e. total soil respiration, root respiration, microbial respiration, and microbial biomass (C and N), and the response of soil respiration to change in temperature. In-field and laboratory (polyhouse) experiments were undertaken. For in-field studies, paired cleared and uncleared pasture plots were selected to represent three major tree communities of the region, i.e. Eucalyptus populnea, E. melanophloia, and Acacia harpophylla. The cleared sites were chosen to represent three different time-since-clearing durations (5, 11-13, and 33 years; n=18 for cleared and uncleared plots) to determine the temporal impact of clearing on soil biological properties. Experiments were conducted in the polyhouse to study in detail the response of soil respiration to changes in soil temperature and soil moisture, and to complement in-field studies for estimating root respiration. The average rate of CO2 emission was 964 g CO2/m2/yr, with no significant difference (P<0.05) among cleared and uncleared sites. Microbial respiration and microbial biomass were greater at uncleared compared with those at cleared sites. The Q 10-value of 1.42 (measured for different seasons in a year) for in-field measurements suggested a small response of soil respiration to soil temperature, possibly due to the limited availability of soil moisture and/or organic matter. However, results from the polyhouse experiment suggested greater sensitivity of root respiration to temperature change than for total soil respiration. Since root biomass (herbaceous roots) was greater at the cleared than at uncleared sites, and root respiration increased with an increase in temperature, we speculate that with rising ambient temperature and consequently soil temperature, total soil respiration in cleared pastures will increase at a faster rate than that in uncleared pastures.
  Kamaljit Kaur , Amit Chhabra and Gurvinder Singh
  Multiprocessor task scheduling is an important and computationally difficult problem. Multiprocessors have emerged as a powerful computing means for running real-time applications especially due to limitation of uni-processor system for not having sufficient enough capability to execute all the tasks. Multiprocessor computing environment requires an efficient algorithm to determine when and on which processor a given task should execute. A task can be partitioned into a group of subtasks and represented as a DAG (Directed Acyclic Graph) that problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system. The problem of mapping meta-tasks to a machine is shown to be NP-complete. The NP-complete problem can be solved only using heuristic approach. The execution time requirements of the applications tasks are assumed to be stochastic. In multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program’s execution time should be minimized. The last job must be completed as early as possible. Genetic Algorithm (GA) is one of the widely used techniques for constrained optimization. Performance of genetic algorithm can be improved with the introduction of some knowledge about the scheduling problem represented by the use of heuristics. In this study the problem of same execution time or completion time and same precedence in the homogeneous parallel system is resolved by using concept of Bottom-level (b-level) or Top-level (t-level). This combined approach named as Modified Genetic Algorithm (MGA) based on MET (Minimum execution time)/Min-Min heuristics and b-level or t-level precedence resolution is finally compared with a pure genetic algorithm, min-min heuristic, MET heuristic and First Come First Serve (FCFS) approach. Results of the experiments show that the modified genetic algorithm produces much better results in terms of quality of solutions.
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