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.

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.

A total of 130 blood samples were collected from the mothers and their newborn babies and examined for malaria parasite using both thin and thick films. Maternal packed cell volume (PCV), and genotype was also determined using haematocrit method and cellulose acetate electrophoresis respectively. The prevalence rate of maternal, fetal, placental and cord parasitaemia were 37(28.46%), 29(22.31%), 33(25.38%) and 30(23.08%) respectively. Malaria infected maternal blood had a mild reduction in PCV level (p<0.05). Genotype showed strong correlation with maternal, fetal, placental and cord parasitaemia (p<0.05). However, the effect of malaria prophylaxis was shown to be more protective for the placental parasitaemia (p<0.

In the tropics, hepatitis C virus (HCV) seroprevalence ranges from 4. 2 % in whole Africa Mother-to-infant transmission of HCV though relatively low, have been reported worldwide and transmission may be intrauterine, intrapartum and post-natal. A descriptive seroepidemiologic study of hepatitis C virus and their associated risk factors have been conducted among pairs of mother and child of pre-~cRoo1 age attending the "well child" clinic of the University of Ilorirr, Teaching Hospital and the in;nsun'lzation clinic of the children specialist hospital, Ilorin. Sera of 70% mother1chPd pdrs were su!.;+.ted to Enzyme-Linked Immunoabsorbent Assay (ELISA) fcr the detection oP antr'bwjies clirected against the core and structural proteins of hepatitis C \ins (anti-ACV). Anti-HCV prevaience of 1.4% was seen among mothers while none of the children was ;rositive for inti-HCV. Scarification appeared to be the rnwt s i g n i r i t risk factors tbat could mssibly contribute to the transmission of HCV among the subjects. The o d y motbcr positive for anti-HCV antibodies had tribal mark scarification while her $year old baby wlro h d w tribal mark wrrr negative. Vaccination has been effective in reducing the incidence of heprtItis B and attending complications of onset of hepatocellular carcinoma later in life; but preventive measares against hepatitis C virus are not yet available

GynaecologistThe study aims to investigate the prevalence of Rubella IgG and IgM with reference to parity status among women of childbearing age attending Federal Socio-demographic data were obtained using structured questionnaire before blood sample collection from the subjects. Rubella screening was done using IgG captured ELISA kits. IgG positive samples were further screened for I 240 evaluated serum samples, 231 (96.25%) were positive, 3 (1.25%) had borderline and 6 (2.5%) were negative to Rub-IgG. Rub-IgM assays revealed 4(1.7%) positivity. Most of the subjects had acquired immunity before age 30. 202(91.0%) of 210(94.6%) monogamous and all recorded subjects with polygamous were positive to rubella IgG (p=0.789) while 3(1.4%) and 1(0.5%) respectively had positive result for rubella IgM (p=0.224). 186(83.6%) of 193(86.9%) and 27(12.2%) of 29(13.1%) within the parous and nulliparous group were positive to Rub-IgG (p=0.266) 4(1.9%) positive and all the subjects under nulliparous group had no current infection (p=0.694). In addition to other risk factors, parity also plays a role in expo to the virus because it increases frequency of contact with the environment and although immunity is high among the subjects. vaccination to avoid near miss or near death experience of the fetus.