Exploring domestic violence in the black minority ethnic (BME) pentecostal community in the South of England.

Over the years, a great deal of effort has been invested in the field of women’s wellbeing and the effects of domestic violence both to the individual and their family; as these are problems that abound in everyday life from nation to nation and to people of all faith and non-believers. From the Literature, it can be noticed that there is a lot of awareness of domestic violence being raised through the media and different organisations standing against domestic violence throughout the UK. There are services, campaigns and many organisations working together in order to raise awareness. However, it appears that there exists a cultural barrier preventing the national awareness from having an impact upon the Black Minority Ethnic (BME) communities. As a female cleric in the BME community, I noted that abuse is a problem experienced by some women and these women depend on the church to meet their spiritual needs, so the church needs to take on the responsibility of eradicating domestic violence in order not to fail the people. The objective of this study is therefore to identify the barriers to creating an awareness of domestic violence in the Pentecostal community and also to identify the best strategies by which an effective awareness of domestic violence can be achieved in this community. The choice of method for this research is that of a Case Study. The data method of collection was qualitative and the styles of collection from study participants were Questionnaire, Focus group and Interview. The outcome of this research is a series of recommendations for change in the domestic violence support service in order to reach the BME Pentecostal community

Intestinal parasites and haemoglobin concentrations in pregnant women attending Braithwaite Memorial Specialist Hospital, Port Harcourt

This study was carried out between January to June, 2014 at Braithwaite Memorial Specialist Hospital, Port Harcourt to determine the haemoglobin level and intestinal parasites in pregnant women attending antenatal clinic of the hospital. In total, 200 stool and blood samples were collected from the women. The stool samples were examined using direct wet preparation and formol ether concentration technique while the haemoglobin levels of the women were estimated using cyanmethaeoglobin technique. The overall prevalence of E. histolytical, A. Lumbricoides, Hookworm Spp, and T. trichuria was 8(4.00), 58(29.00), 8(4.00) and 8(4.00) respectively. The proportion of pregnant women with helminithic infestation was 60 (30.0%) while their mean Heamoglobin concentration was 10.85+0.32g/dl while the pregnant women without helminthic infestation (140(70.0%) had Heamoglobin concentration of 11.03+0.16g/dl (P<0.05). The study has shown that pregnant women with intestinal parasites had low haemoglobin level. This may be attributed to poor socioeconomic status of the women coupled with poor environmental sanitation and lack of clean portable water supply may have contributed to the high prevalence

Dehydroepiandrosterone (DHEA) and Testosterone levels in infertile males attending Madonna University Teaching Hospital, Elele

Aim: The study was carried out to determine the concentrations of Dehydroepiandrosterone hormone (DHEAS) and testosterone in infertile males and compared with fertile males attending Madonna University Teaching Hospital (MUTH) Elele. Methods: Thirty apparently infertile males were studied and compared with 30 apparently fertile male as control.Results: There was significant increase (P<0.05) in DHEAS of 1.23+0.07 ug/ml obtained in infertile male compared with 3.78 +0.13 ug/ml in the control. There was significant difference in Semen count of 56.27 +2.82million/ml in fertile males compared with 7.73+ 0.10 million/ml while testosterone in infertile males of 2.53+0.09 was significantly lower than 7.52+0.31 in fertile males(P<0.05).Conclusion: The study showed that DHEAS is elevated in infertility hence should be considered an indicator of infertility

Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models

The continued reliance on machine learning algorithms and robotic devices in the medical and engineering practices has prompted the need for the accuracy prediction of such devices. It has attracted many researchers in recent years and has led to the development of various ensembles and standalone models to address prediction accuracy issues. This study was carried out to investigate the integration of EKF, RBF networks and AdaBoost as an ensemble model to improve prediction accuracy. In this study we proposed a model termed EKF-RBFN-ADABOOST

A TECHNICAL SURVEY OF POST HARVEST PROCESSING OPERATIONS OF CASSAVA IN EKITI STATE, SOUTH-WEST NIGERIA

Cassava (Manihot esculenta Crantz) is a staple root crop of the tropics. It is a major source of energy in both human and livestock diets. However, cassava is highly perishable and must be converted into stable products soon after it is harvested. This study was therefore conducted to examine the present level of postharvest processing operations of cassava in Ekiti State, South-West Nigeria. The study was carried out using a Participatory Rural Appraisal (PRA) technique. Accordingly, twelve out of the sixteen local government areas of the State were randomly selected for the study. A purposive random sampling approach was used for selecting farmers for participation in the study. Structured questionnaires were administered to 10 cassava processors per LGA to make a total of 120 respondents for the study area. In addition, the method of Focus Group Discussion (FGD) was used to obtain information on the method and level of technologies used for post-harvest processing of cassava in the area. Data were analysed using descriptive statistics. Results show that the products of cassava processing in the study area are garri, fufu, lafun and starch. 58.3% of the cassava processors process between 500 to 2500 kg of fresh cassava tubers per day with 68.3% of the processors employing a combination of traditional and modern methods to perform their processing operations. 71.6% of the respondents use head pans to move cassava tubers from point of purchase to processing facilities. The survey revealed a near absence of mechanized processing of cassava in the study area due to lack of funds by the individual processors to acquire the necessary processing machines. Accordingly, it is recommended that cluster processing facilities should be established which would allow a processor to carry out their unit operations for a fee without necessarily owning the processing equipment

Iris feature extraction: a survey

Biometric as a technology has been proved to be a reliable means of enforcing constraint in a security sensitiveenvironment. Among the biometric technologies, iris recognition system is highly accurate and reliable becauseof their stable characteristics throughout lifetime. Iris recognition is one of the biometric identification thatemploys pattern recognition technology with the use of high resolution camera. Iris recognition consist of manysections among which feature extraction is an important stage. Extraction of iris features is very important andmust be successfully carried out before iris signature is stored as a template. This paper gives a comprehensivereview of different fundamental iris feature extraction methods, and some other methods available in literatures.It also gives a summarised form of performance accuracy of available algorithms. This establishes a platform onwhich future research on iris feature extraction algorithm(s) as a component of iris recognition system can bebased.Keywords: biometric authentication, false acceptance rate (FAR), false rejection rate (FRR), feature extraction,iris recognition system

Effect of Thermal Treatment on Chemical, Biological and Mechanical Properties of African Whitewood (Triplochiton scleroxylon K. SCHUM)

The thermal modification of wood is a potential alternative method for improving wood properties. This paper evaluates the effect of thermal treatment on the chemical, biological and mechanical properties of African Whitewood (Triplochiton scleroxylon K. Schum) by subjecting the wood to three temperature (200, 170, 140 ℃) at three different time (3, 6, 12 h). Fourier Transformed infrared (FT-IR) spectrometer was used to ascertain the effect of heat treatment on the chemical constituent of the wood while mechanical properties were determined by Modulus of Elasticity (MOE) and Modulus of Rupture (MOR). Durability of heat-treated wood against subterranean termites using field test was also investigated.  FTIR results provided information on the chemical constituents after heat treatment revealing the cellulose and hemicelluloses of Triplochiton scleroxylon wood samples. The decreasing intensity of hydroxyl groups stretching at 3341 cm-1 indicated that heat-treated samples have lost some of their hydroxyl groups. The results showed that the MOR of heated-treated wood at 200 ℃ was significantly higher than the untreated samples. There was also a 26% increase in the MOE of the 170 ℃ heat-treated wood relative to untreated samples. Wood samples thermally treated at 170 ℃ and 200 ℃ as well as untreated wood samples were strongly degraded in term of weight loss (WL) by termite though the degree of degradation varied based on temperature and time in which the wood was modified. However, heat-treated wood at 140 ℃ gave the lowest WL of 20.41% compared to other treated and untreated wood samples. Therefore, strength properties and termites’ resistance of African whitewood also known as Obeche wood in Nigeria could be improved when thermally modified at 170 ℃ and 140 ℃ respectively

Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods

This paper presents the empirical comparison of boosting implementation by reweighting and resampling methods. The goal of this paper is to determine which of the two methods performs better. In the study, we used four algorithms namely: Decision Stump, Neural Network, Random Forest and Support Vector Machine as base classifiers and AdaBoost as a technique to develop various ensemble models. We applied 10-fold cross validation method in measuring and evaluating the performance metrics of the models. The results show that in both methods the average of the correctly classified and incorrectly classified are relatively the same. However, average values of the RMSE in both methods are insignificantly different. The results further show that the two methods are independent of the datasets and the base classier used. Additionally, we found that the complexity of the chosen ensemble technique and boosting method does not necessarily lead to better performance