Abstract: This study investigated the digestibility of Brachiaria ruziziensis and Centrosema pascuorum hay mixtures at varying proportions in Yankasa rams. Twelve Yankasa rams with average initial weight 10.25 ± 0.1 kg were assigned to three dietary treatments of B. ruziziensis and C. pascuorum hay at different mixtures (75BR:25CP, 50BR:50CP and 25BR:75CP, respectively) in a Completely Randomized Design (CRD) for a period of 14 days. Concentrate diet was given to the experimental animals as supplement at fixed proportion, while the forage mixture (basal diet) was fed at 3% body weight. Animals on 50BR:50CP had better nutrient digestibility (crude protein, acid and neutral detergent fibre, ether extract and nitrogen free extract) than other treatment diets, except in dry matter digestibility (87.35%) which compared with 87.54% obtained in 25BR:75CP treatment diet and also organic matter digestibility. All parameters taken on nitrogen balance with the exception of nitrogen retained were significantly higher (P < 0.05) in animals fed 25BR:75CP diet, but were statistically similar with values obtained for animals on 50BR:50CP diet. From results obtained in this study, it is concluded that mixture of 25%BR75%CP gave the best nutrient digestibility and nitrogen balance in Yankasa rams. It is therefore recommended that B. ruziziensis and C. pascuorum should be fed at 50:50 mixture ratio for enhanced animal growth and performance in Nigeria.
Abstract: Leukaemia is a blood cancer disease that contributes
to the increment of mortality rate in Malaysia each year. There are
two main categories for leukaemia, which are acute and chronic
leukaemia. The production and development of acute leukaemia cells
occurs rapidly and uncontrollable. Therefore, if the identification of
acute leukaemia cells could be done fast and effectively, proper
treatment and medicine could be delivered. Due to the requirement of
prompt and accurate diagnosis of leukaemia, the current study has
proposed unsupervised pixel segmentation based on clustering
algorithm in order to obtain a fully segmented abnormal white blood
cell (blast) in acute leukaemia image. In order to obtain the
segmented blast, the current study proposed three clustering
algorithms which are k-means, fuzzy c-means and moving k-means
algorithms have been applied on the saturation component image.
Then, median filter and seeded region growing area extraction
algorithms have been applied, to smooth the region of segmented
blast and to remove the large unwanted regions from the image,
respectively. Comparisons among the three clustering algorithms are
made in order to measure the performance of each clustering
algorithm on segmenting the blast area. Based on the good sensitivity
value that has been obtained, the results indicate that moving kmeans
clustering algorithm has successfully produced the fully
segmented blast region in acute leukaemia image. Hence, indicating
that the resultant images could be helpful to haematologists for
further analysis of acute leukaemia.
Abstract: The generation of an idea that goes through several
phases is affected by individual factors, interests, preferences and
motivation. The purpose of this research was to analyze the
difference in difficulties of generating ideas according to individual
learning styles. A total of 375 technical students from four technical
universities in Malaysia were randomly selected as samples. The
Kolb Learning Styles Inventory and a set of developed questionnaires
were used in this research. The results showed that the most dominant
learning style among technical students is Doer. A total of 319
(85.1%) technical students faced difficulties in solving individual
assignments. Most of the problem faced by technical students is the
difficulty of generating ideas for solving individual assignments.
There was no significant difference in difficulties of generating ideas
according to students’ learning styles. Therefore, students need to
learn higher order thinking skills enabling students to generate ideas
and consequently complete assignments.
Abstract: In the world of Peer-to-Peer (P2P) networking
different protocols have been developed to make the resource sharing
or information retrieval more efficient. The SemPeer protocol is a
new layer on Gnutella that transforms the connections of the nodes
based on semantic information to make information retrieval more
efficient. However, this transformation causes high clustering in the
network that decreases the number of nodes reached, therefore the
probability of finding a document is also decreased. In this paper we
describe a mathematical model for the Gnutella and SemPeer
protocols that captures clustering-related issues, followed by a
proposition to modify the SemPeer protocol to achieve moderate
clustering. This modification is a sort of link management for the
individual nodes that allows the SemPeer protocol to be more
efficient, because the probability of a successful query in the P2P
network is reasonably increased. For the validation of the models, we
evaluated a series of simulations that supported our results.