Biochemical evaluation of Gmelina arborea fruit meal as a swine feedstuff

An experiment was conducted to evaluate the influence of Gmelina arborea fruits (GAF) meal on haematology and certain biochemical parameters including blood enzyme profile of wean pigs. 16-piglets, 8-males and 8-females averaging 12.41 ± 0.59 kg live weight from Hampshire commercial breed were allotted to four dietary treatment groups each consisting of four piglets per treatment group in a completely randomised design. The diets formulated on iso-nitrogenous and iso-energetic basis had Diet 1 containing 30% processed GAF meal and was taken as a reference Diet while Diets 2, 3 and 4 contained 10, 20 and 30% raw GAF meal respectively. The experimental diets and water were supplied to appetite in a feeding trial which lasted for 28 days. There were no statistically significant differences in haematological parameters (p > 0.05) except lymphocytes and neutrophils of the leucocytes differential count (p < 0.05). There were also no significant differences in the selected blood enzymes and serum biochemical parameters of the trial animal models (p >0.05). Urine analyses similarly showed no significant difference in urea and creatinine excretions except that there was a significant difference in uric acid produced (p < 0.05). An overall assessment of the study indicated that values of some parameters measured tended to decrease (in case of blood indices and serum constituents) and increase (with regards to blood urea nitrogen, creatinine in blood and urine and uric acids) though not significantly as the dietary inclusion level of raw GAF meal increased. It was therefore concluded that GAF when processed or incorporated at lower levels has no adverse effect on animals.Keywords: Gmelina arborea, diets, pi

Use of probiotics for sustainable aquaculture production in Nigeria

Aquaculture is fast developing in Nigeria but to ensure a sustainable development there is need to address problem of diseases which is an important issue affecting the aquaculture production. Though the use of antimicrobial drugs has helped in some ways, the notorious effects of antibiotics has necessitated seeking an alternative that is environmental friendly and safe for the organisms and consumers. Probiotics has been established to be a good alternative and its use is now gaining acceptance. This review aims to define the concept of probiotics, highlights the process of isolation and methods of application as well as its current status, challenges and prospects in Nigeria. Probiotics are entire or components of microorganisms that are beneficial to the health of a host. They are naturally present in the organism and or the culture medium and have different mechanisms of action. They are usually isolated from the gill, skin or culture medium and pass through isolation processes to obtain the desired strains and applied in-vitro or in-vivo. Probiotics is a natural ingredient in finfish, shellfish and culture environment and its appropriate application will save Nigeria aquaculture from losses due to diseases. It will make available, aquaculture products that are safe for consumption as well ensuring a healthy aquatic environment. However research should be conducted to make available, products that suit the local species and environment in commercial forms. Also, safety issues should be considered at all time.Key words: sustainable production, fish culture, probiotics, antibiotics, diseases.

Evaluation of the toxicological status of Balanite aegyptiaca seed oil

A total of fourteen rats were fed with diets containing either 10 % groundnut oil (control group) or 10 % Balanites aegyptiaca seed oil (experimental group) for six weeks. At the end of the experiment, the animals were sacrificed and blood samples and some organs of the rats in both groups were collected for analysis. The albino rats appeared to suffer no toxicological effect and weekly monitoring of the rats showed good physical appearance. The rats in the experimental groups displayed fairly similar body weight gain when compared with those from the control group. There was no significant difference between the haematological and histopathological results obtained for both the experimental and control groups except for the liver of two of the rats in the experimental that showed some lesion. There might be need for refining of the seed oil before it can be safe for animal/human consumption. Key words: Balanites aegyptiaca; feed; haematological analysis; seed oi

Bayesian Semi-Parametric Modeling of Infertility in Nigeria

Infertility in Nigeria is a neglected reproductive health issue despite its negative impact. Majority of infertility-related research has focused on treating the consequences of infertility rather than investigating the determinantsto explain the spatial and spline effect of infertility in the country. This work is aimed at investigating spatial variation of determinants of infertility among female in Nigeria. The finding reveals that women at reproductive age have a high probability of infertility in some southern part of Nigeria astheir ages are steadily increasing. Also, change in the characteristics of place of residence and source of water increase the chance of woman being infertile. Policy makers on health sectors should make effort to address problems of climatic and atmospheric change in the identified social and demographic risk factors

PREDICTING STUDENTS«¤?? ENROLLMENT USING GENERALIZED FEED-FORWARD NEURAL NETWORK

An important obligation of educational planning is the projection of students«¤?? enrollment which forms the basis for many of the investment decisions. Enrollment projection provides information for decision making and budget planning hence, it is important to the development of higher education. As many factors have impacts on the enrollment number, and for the above reasons, students«¤?? population and enrollment number should be considered as a chaotic system. In this research, a Generalized Feed-Forward Neural Network (GFFNN) for students«¤?? enrollment prediction was proposed. The architecture of the proposed model was in-line with eight steps involved in developing a neural network model for predicting a chaotic system. The data used was obtained from Academic Planning and Quality Control Unit of Tai Solarin University of Education, Ogun State Nigeria. The results from the study showed that the mean absolute percent error of GFFNN has an average of 0.0101% unlike linear regression and autoregression models that were compared with it, with an average of 0.0570% and 0.0725% respectively. The proposed methodology is expected to assist the school management to adequately plan for the future needs of the students in the provision of facilities.ª¤

PREDICTING STUDENTS«¤?? GRADE SCORES USING TRAINING FUNCTIONS OF ARTIFICIAL NEURAL NETWORK

The observed poor quality of graduates of some Nigerian Universities in recent times has been traced to non-availability of adequate mechanism. This mechanism is expected to assist the policy maker project into the future performance of students, in order to discover at the early stage, students who have no tendency of doing well in school. This study focuses on the use of artificial neural network (ANN) model for predicting students«¤?? academic performance in a University System, based on the previous datasets. The domain used in the study consists of sixty (60) students in the Department of Computer and Information Science, Tai Solarin University of Education in Ogun State, who have completed four academic sessions from the university. The codes were written and executed using MATLAB format. The students«¤?? CGPA from first year through their third year were used as the inputs to train the ANN models constructed using nntool and the Final Grades (CGPA) served as a target output. The output predicted by the networks is expressed in-line with the current grading system of the case study. CGPA values simulated by the network are compared with the actual final CGPA to determine the efficacy of each of the three feed-forward neural networks used. Test data evaluations showed that the ANN model is able to predict correctly, the final grade of students with 91.7% accuracy.ª¤

An Improved Technique for Multi-Dimensional Constrained Gradient Mining

Multi-dimensional Constrained Gradient Mining, which is an aspect of data mining, is based on mining constrained frequent gradient pattern pairs with significant difference in their measures in transactional database. Top-k Fp-growth with Gradient Pruning and Top-k Fp-growth with No Gradient Pruning were the two algorithms used for Multi-dimensional Constrained Gradient Mining in previous studies. However, these algorithms have their shortcomings. The first requires construction of Fp-tree before searching through the database and the second algorithm requires searching of database twice in finding frequent pattern pairs. These cause the problems of using large amount of time and memory space, which retrogressively make mining of database cumbersome.  Based on this anomaly, a new algorithm that combines Top-k Fp-growth with Gradient pruning and Top-k Fp-growth with No Gradient pruning is designed to eliminate these drawbacks. The new algorithm called Top-K Fp-growth with support Gradient pruning (SUPGRAP) employs the method of scanning the database once, by searching for the node and all the descendant of the node of every task at each level. The idea is to form projected Multidimensional Database and then find the Multidimensional patterns within the projected databases. The evaluation of the new algorithm shows significant improvement in terms of time and space required over the existing algorithms.  &nbsp

Family characteristics, students' reading habits, environment and students' academic performance in Nigeria

The paper examined family background factors that affect students' academic achievement in institutions of higher learning in Nigeria. With the use of structured questionnaire, data were collected from 110 first-degree final year students using random sampling and analysed through multiple linear regression techniques. It was found that student's academic performance was positively influenced by student's parental level of education, maternal income level, age, income of the student and number of hours allocated for reading on daily basis. Those students who spent more hours reading their books daily were found performing better than those who spent lesser hours. The hypothesis that parental educational level impacted positive effects on students' academic performance was confirmed valid for the country while effects of parental occupation and parental income were mixed. The major finding of the paper was that higher educational attainment and income status of parents were essential factors contributing to high academic record of students of tertiary institutions. It was, therefore, recommended that policy that enforces higher education advancement for all parents should be enforced in Nigeria

PREDICTING SOCIAL NETWORK ADDICTION USING VARIANT SIGMOID TRANSFER FEED-FORWARD NEURAL NETWORKS (FNN-SNA)

Researchers have reflected on personal traits that may predict Social Networking Sites (SNS) addiction. However, most of the researchers involved in the findings of personality traits predictor for social networking addiction either postulate or based their conclusions on analytical tools. Moreso, a review of the literature reveals that the prediction of social networking addiction using classifiers have not been well researched. We examined the prediction of SNS addiction from a well-structured questionnaire consisting of sixteen (16) personality traits. The questionnaire was administered on the google form with a response rate of 95% out of the 102-sample size. Additionally, a three (3) variant sigmoid transfer feed- forward neural networks was developed for the prediction of SNS addiction. Result indicated that pertinence (β = 0.251, p  0.01) was the most powerful predictor of social networking addiction in general and less obscurity addiction (β = 0.244, p  0.01). Experimental results also showed that the developed classifier correctly predict SNS addiction with 98% accuracy compared to similar classifiers.     &nbsp