A study to evaluate the insecticidal properties of some plants was undertaken. Powder and aqueous extracts of Neem, Azadirachta indica, False sesame, Ceratotheca sesamoides and the Physic nut, Jatropha curcas were evaluated as grain protectants against the cowpea seed beetle, Callosobruchus maculatus (F.) in the laboratory at 1.5, 2.0 and 2.5 (% v/w) concentrations per 20g of cowpea seeds. Seeds of Ifebrown cowpea variety were used for the experiment. Aqueous extracts and seed powder of the plant materials were applied to the cowpea seeds using the contact method of application in the laboratory. Results revealed 2.0 % v/w and 2.5 % v/w had significant increase (P< 0.05) in adult mortality of C. maculatus in both the powder and aqueous treatments of J. curcas. Oviposition, adult emergence and percentage grain weight damage decreased significantly (P < 0.05) in a proportionate, dose dependent manner. Although C. sesamoides was the least effective when compared with the other treatments, it was significantly better than the control in the protection of stored cowpea. There was no significant difference between treatments in the germination percentage of the seeds and there was no observed discolouration of the treated seeds. All the test plant materials (Aqueous and powder) of all the test plants effectively reduced the weight loss of cowpea treated seeds with J. curcas followed by A. indica at 2.5% being the most effective. Farmers in developing countries can use J. curcas and A. indica as an alternative to chemical pesticide in rural grain storage.
This paper presents a survey of three iterative methods for the solution of linear equations has been evaluated in this work. The result shows that the Successive Over-Relaxation method is more efficient than the other two iterative methods, considering their performance, using parameters as time to converge, number of iterations required to converge, storage and level of accuracy. This research will enable analyst to appreciate the use of iterative techniques for understanding linear equations. @ JASEM The direct methods of solving linear equations are known to have their difficulties. For example the problem with Gauss elimination approach lies in control of the accumulation of rounding errors Turner, (1989). This has encouraged many authors like Rajase Keran (1992), Fridburd et al (1989), Turner (1994) Hageman et al (1998 and Forsyth et al (1999) to investigate the solutions of linear equations by direct and indirect methods. Systems of linear equations arise in a large number of areas both directly in modeling physical situations and indirectly in the numerical solutions of the other mathematical models. These application occur in virtually all areas of the physical, biological and social science. Linear systems are in the numerical solution of optimization problems, system of non linear equations and partial differential equations etc. The most common type of problem is to solve a square linear system AX = b ---------------------(1) of moderate order with coefficient that are mostly non zero, such linear system of any order are called dense since the coefficient matrix A is generally stored in the main memory of the computer in order to efficiently solve the linear system, memory storage limitations in most computer will limit the system to be less than 100 to 200 depending on the computer. The efficiency of any method will be judged by two criteria Viz: i) How fast it is? That is how many operations are involved. ii) How accurate is the computer solution.Because of the formidable amount of computations required to linear equation for large system, the need to answer the first questions is clear. The need to answer the second, arise because small round off errors may cause errors in the computer solution out of all proportion to their size. Furthermore, because of the large number of operations involved in solving high-order system, the potential round off errors could cause substantial loss of accuracy. Generally, the matrices of coefficient that occur in practice fall into one of two categories. a. Filled but not large:-This means that there are few zero elements, but not large, that is to say a matrix of order less than 100. Such matrices occur in a wide variety of problems e.g. engineering are statistics etc.b. Sparse and perhaps very large:-In contrast to the above a sparse matrix has few non zero elements, very large matrix of order say one thousand. Such matrices arise commonly in the numerical solution of partial differential equations.
Laboratory experiments were conducted to investigate the potentials of four different vegetable oils (olive oil, groundnut oil, soybean oil and palm kernel oil) for the protection of stored cowpea against Callosobruchus maculatus. Ife-brown seeds (a susceptible variety) used for the experiment were subjected to the different oil treatments applied at 0.2 ml per 50 g of seeds. The experiment was laid out in a completely randomised design with three replicates per treatment. All the oils tested suppressed the development of C. maculatus to some extent with groundnut oil and palm kernel oil exhibiting similar results in the control of the pest. There was no significant difference between palm kernel oil and groundnut oil for adult mortality, larvae and pupae emergence at P< 0.05 but it was significantly different for the F1 progeny emergence. Palm kernel oil was more effective against the F1 progeny emergence. These two oils could be used in the storage of cowpea against C. maculatus.
This paper presents a survey of three iterative methods for the solution of linear equations has been evaluated in this work. The result shows that the Successive Over-Relaxation method is more efficient than the other two iterative methods, considering their performance, using parameters as time to converge, number of iterations required to converge, storage and level of accuracy. This research will enable analyst to appreciate the use of iterative techniques for understanding linear equations. @ JASEM The direct methods of solving linear equations are known to have their difficulties. For example the problem with Gauss elimination approach lies in control of the accumulation of rounding errors Turner, (1989). This has encouraged many authors like Rajase Keran (1992), Fridburd et al (1989), Turner (1994) Hageman et al (1998 and Forsyth et al (1999) to investigate the solutions of linear equations by direct and indirect methods. Systems of linear equations arise in a large number of areas both directly in modeling physical situations and indirectly in the numerical solutions of the other mathematical models. These application occur in virtually all areas of the physical, biological and social science. Linear systems are in the numerical solution of optimization problems, system of non linear equations and partial differential equations etc. The most common type of problem is to solve a square linear system AX = b ---------------------(1) of moderate order with coefficient that are mostly non zero, such linear system of any order are called dense since the coefficient matrix A is generally stored in the main memory of the computer in order to efficiently solve the linear system, memory storage limitations in most computer will limit the system to be less than 100 to 200 depending on the computer. The efficiency of any method will be judged by two criteria Viz: i) How fast it is? That is how many operations are involved. ii) How accurate is the computer solution.Because of the formidable amount of computations required to linear equation for large system, the need to answer the first questions is clear. The need to answer the second, arise because small round off errors may cause errors in the computer solution out of all proportion to their size. Furthermore, because of the large number of operations involved in solving high-order system, the potential round off errors could cause substantial loss of accuracy. Generally, the matrices of coefficient that occur in practice fall into one of two categories. a. Filled but not large:-This means that there are few zero elements, but not large, that is to say a matrix of order less than 100. Such matrices occur in a wide variety of problems e.g. engineering are statistics etc.b. Sparse and perhaps very large:-In contrast to the above a sparse matrix has few non zero elements, very large matrix of order say one thousand. Such matrices arise commonly in the numerical solution of partial differential equations.
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