Modelling the association between in vitro gas production and chemical composition of some lesser known tropical browse forages using artificial neural network

In vitro gas production of four different browse plants (Azadirachta indica, Terminalia catappa, Mangifera indica and Vernonia amygdalina) was investigated under different extractions. The relationship between the forage composition parameters (dry matter, organic matter, crude protein, acid detergent fibre, neutral detergent fibre and acid detergent lignin), process parameters (extraction mode and incubation time), and volume of gas production were modelled with artificial neural network (ANN). The ANN model consisted of simple, multi-layered, back-propagation networks with eight input neurons consisting of the composition and process parameters and one output neuron for the gas volume. The networks were trained with different algorithms and varying number of layer and neuron in the hidden layer to determine the optimum network architecture. The network with single hidden layer having 45 ‘tangent sigmoid’ neurons trained with Livenberg-Marquard algorithm combined with ‘early stopping’ technique was found to be the optimum network for the model with R-value: mean = 0.9504; max. =0.9618; min. = 0.9343; and std. = 0.0059. The influence of each chemical composition and processing parameters on gas production was simulated. The developed ANN model offers a more cost and timeefficient strategy in feed evaluation for ruminant animals

Antimicrobial potential of extracts and fractions of the African walnut – Tetracarpidium conophorum

Antibacterial and antifungal evaluation of the leaf, stem bark, kernel and root methanol extracts as well as the hexane, chloroform, ethyl acetate and methanol fractions of the leaf of Tetracarpidium conophorum, the African walnut, were investigated using the agar cup diffusion and agar broth dilution techniques. Extracts and fractions were tested against four clinical strains of 2 Gram positive, 2 Gram negative bacteria and two of fungi. They exhibited concentration-dependent antimicrobial properties. The extracts displayed higher activities to the Gram positive organisms. The edible nut was devoid of any antimicrobial property. The leaf extract was most active and it inhibited the growth of all the microorganisms used in the study. This led to the bioassay-guided fractionation of the leaf methanol extract and the ethyl acetate fraction of the leaf extract, displayed higher activities with the bacteria and fungi used in the assay, at the five test concentrations (100 mg – 10 mg/ml). Pseudomomas aeruginosa and Candida albicans were most sensitive to the extracts. Ampicillin and tioconazole were used aspositive control, and methanol, used as negative control. The plant materials were also screened for secondary metabolites and this indicated the presence of alkaloids, saponins and tannins and absenceof cardiac glycosides. The thin layer chromatographic analysis of the ethyl acetate fraction of the leaf crude extract confirmed the presence of alkaloids and tannis. These could be responsible for observedactivity in the leaf of the plant; thus justifying its traditional uses especially in the treatment of dysentery

Intelligent Tool Condition Monitoring In High-Speed Turning Of Titanium Ti-6Al-4V Alloy

Intelligent Tool Condition Monitoring (TCM) is an essential requirement in the drive towards automated machining operations. In this paper, a Multi-Layered Perceptron (MLP) neural net-work model has been developed for on-line condition monitoring of tool wear in high-speed turning of Titanium-based alloy (Ti-6Al-4V). Machining trials were conducted for typical rough and finish turning operations with cutting speed (90 – 120 m/min), feed rate (0.15 – 0.2 mm/rev), and depth of cut (0.5 -2.0 mm) using uncoated cemented carbide (K10 grade) inserts with Inter-national Standard Organization (ISO) designation “CNMG 120412”. The tool maximum flank wear (VBmax), cutting forces (feed force, Fx, and tangential force, Fz), and spindle motor power were measured during each machining operation. The cutting parameters (cutting speed, feed rate, and depth of cut), and cutting force and spindle power were used in isolation or in combi-nation as input dataset in training the neural network to predict wear land on cutting tool at different stages of wear propagation (light, medium and heavy). The neural network model was designed using Matlab® neural toolbox. Accuracy of model for the prediction of tool wear at dif-ferent wear stages were evaluated based on the Percentage Error (PE) for both roughing and finishing operations. Results showed that, the heavy wear stage (PE = ±5%) was predicted more accurately compared to the light (PE = +5 to -10%) and medium (PE = +25 to -30%) wear stages. The combination of the force, power signals and cutting parameters improved perform-ance of the model.Keywords: Artificial neutral network, Turning, Ti-6Al-4V alloy; Tool wear, Condition monitorin

Modelling and Forecasting Periodic Electric Load for a Metropolitan City in Nigeria

In this work, three models are used to analyze the electric load capacity of a fast growing urban city and to estimate its future consumption. Ikorodu, the case-study location is a highly populated city whose energy demand is continuously increasing. The ultimate focus of this study is to establish a basis for the comparison of different electric load consumption for the existing populace and to provide estimates for the future planning of the city. In this work, three different models have been used to present more accurate load predictions and to enhance proper comparison of results. Among numerous mathematical and scientific models that are applicable to this kind of task, the compound-growth method, the linear model approach and the cubic model have been chosen to enhance diversity in load analysis. The futuristic scheme to be harnessed will fall within the ranges of values obtained from the three different models used in forecasting. This paper concludes with issues pertaining to economics of load utilization as it affects substantive planning.Key words: Electric-load, Linear trend, Compound-growth, Cubic model, forecastin

Malignant perivascular epithelioid cell tumor of the uterus

Perivascular epithelioid cell tumors (PEComas) are a rare collection of tumors arising in a wide array of anatomic locations and characterized by a myomelanocytic phenotype. PEComas which occur in non-classic anatomic distributions are known as perivascular epithelioid cell tumor-not otherwise specified (PEComa-NOS), and one of the most common primary sites for PEComa-NOS is the uterus. The risk of aggressive behavior of these tumors has been linked to a number of factors evaluable on pathologic review following initial surgical resection. We report a case of PEComa-NOS of the uterus with multiple high-risk features, including frank vascular invasion, with no evidence of recurrent disease 18 months following initial surgical resection

Activity of Trichilia megalantha Harms and Trichilia welwitschii CDC Extracts and Fractions on Anopheles gambiae Larvae

Background: Lately, there is an increasing shift towards the use of environmentally friendly and biodegradable natural vector control of plant origin as agent for disease vector control. Objectives:  To determine the toxicity of extracts and fractions of T. megalantha and T. welwitschii on Anopheles gambiae larvae. Methods: The larvicidal activity of methanol extracts of leaf, stem bark and root bark of Trichilia megalantha and Trichilia welwitschii (Meliaceae) and fractions of stem bark of T. megalantha and root of T. welwitschii were evaluated on early 4th instar Anopheles gambiae larvae. Larvae were exposed to various concentration of plant extracts and fractions. Dead larvae were counted after 24 h of exposure. The most active extracts of both plants were partitioned into hexane, dichloromethane (DCM), ethyl acetate and methanol and subjected to the same assay.  Ethanol (5%) was included as a negative control. The experiments were done in triplicate. Results were compared to those of larvae exposed to N,N-diethyl-3-methylbenzamide (LC50 =1000.09 µg.mL-1), the reference insecticide. Results: All tested extracts and fractions showed larvae mortality. Of the six crude extracts screened, T. megalantha stem bark showed the highest activity with LC50 of 15.6 µg.mL-1 while the leaf (LC50 =496.1 µg.mL-1) showed the least activity. The root of T. welwitschii was more toxic to the larvae (LC50 =65.0 µg.mL-1) while the leaf (LC50 =232.0 µg.mL-1) showed the least larvae toxicity. Conclusion: The results showed that the extracts and fractions of T. megalantha and T. welwitschii were significantly toxic to Anopheles gambiae larvae.  The promising activity demonstrated by the two plants in mosquito vector control could contribute significantly to malaria control Keywords: Plant extract, Meliaceae, larvicidal, vector contro

Application of Neuro-Fuzzy to palm oil production process

Palm oil is an important nutritional food requirement and in order to facilitate the production of palm oil for consumption, the production process of palm oil has been investigated. The basic operations involved in the production of edible palm oil include; purchase, transportation and reception of oil palm bunches; bunch threshing and fruit fermentation; sorting and weighing of oil palm fruits; boiling, digestion and pressing of palm oil fruits; clarification and drying of palm oil and palm oil storage. A Neuro–Fuzzy model was used to analyze the performance of palm oil production process as it affects the basic operations involved in the production of edible palm oil. The research work can be applied to any other small or medium scale production firm for better efficiency.Journal of the Nigerian Association of Mathematical Physics, Volume 15 (November, 2009), pp 363 - 37