Spatial and Temporal Variation in Water Quality of River Benue, Nigeria

The study investigates the spatial and temporal variation in water quality parameters at ten different locations along River Benue for twelve consecutive months. In order to explore the spatial variation among different stations and seasonal changes, multivariate analysis of variance (MANOVA) was used to group these on the basis of spatial similarities. MANOVA on season and station shows that there is no significant difference between the stations investigated while there is for the seasons. This could be viewed as a resulting from the narrow spatial sampling interval (12 km at 0.7% total length of River Benue). However, discriminate analysis identified all the parameters to discriminate between the three seasons with 99.2% correct assignations. Two discriminate functions were found and the total variance cumulative was 100% between seasons. The first function explained 64.8% of the total variance between the seasons while the second function explained 35.2%. Total solids (TS) were the highest contributor in discriminate functions 1 and 2. Therefore, discriminate function analysis would enable us to predict the likely season a water sample from metropolitan Makurdi was collected given the values of the water quality parameters. It also enables us to conclude that all the parameters were responsible for significant seasonal variations in River Benue water quality.

A Well-Built Hybrid Recommender System for Agricultural Products in Benue State of Nigeria

Benue State of Nigeria is tagged the Food Basket of the country due to its heavy production of many classes of food. Situated in the North Central Geo-Political area of the country, its food production ranges from root crops, fruits to cereals. Recommender systems (RSs) allow users to access products of interest, given a plethora of interest on the Internet. Recommendation techniques are content-based and collaborative filtering. Recommender systems based on collaborative filtering outshines content-based systems in the quality of their recommendations, but suffers from the cold start problem, i.e., not being able to recommend items that have few or no ratings. On the other hand, content-based recommender systems are able to recommend both old and new items but with low recommendation quality in relation to the user’s preference. This work combines collaborative filtering and content based recommendation into one system and presents experimental results obtained from a web and mobile application used in the simulation. The work solves the problem of serendipity associated with content based (RS) as well as the problem of ramp-up associated with collaborative filtering. The results indicate that the quality of recommendation is promising and is competitive with collaborative technique recommending items that have been seen before and also effective at recommending cold-start products.

A Review of Activity Time Distributions in Risk Analysis

Project Evaluation and Review Technique (PERT) alongside recent modifications is a popular and useful tool in project risk analysis. Over the past seven decades, there have been some modifications in PERT owing to the shift from beta distributed activity times to other activity time distributions. This paper presents a review of activity time distributions in risk analysis as found in literature up to date.

Analysis of Pulsatile Magnetohydrodynamic (MHD) Third Grade Blood Flow in a Stenosed Artery

In this research, we modeled MHD third grade blood flow in a stenosed artery. The blood viscosity and the density have been modeled into the shear thinning/thickening parameters, the most important rheological properties of blood. We used regular perturbation method and obtained the flow characteristics such as the flow velocity, the volume flow rate, the shear stress and the resistance to the flow considering a single layered stenosed artery. The results however showed that there is significant increase in volume flow rate and the velocity with increase in the magnetic field intensity H and the shear thinning Λ and reduces with increase in the shear thickening Ω.

A Neural Based Experimental Fire-Outbreak Detection System for Urban Centres

Incessant fire-outbreak in urban settlements has remained intractable especially in developing countries like Nigeria. This is often characterized by grave socio-economic aftermath effects. Urban fire outbreak in Nigerian cities has been on increase in recent times. The major problem faced by fire fighters in Nigerian urban centres is that there are no mechanisms to detect fire outbreaks early enough to save lives and properties. They often rely on calls made by neighbours or occupants when an outbreak occurs and this accounts for the delay in fighting fire outbreaks. This work uses Artificial Neural Networks (ANN) with backpropagation method to detect the occurrence of urban fires. The method uses smoke density, room temperature and cooking gas concentration as inputs. The work was implemented using Java programming language and results showed that it detected the occurrence of urban fires with reasonable accuracy. The work is recommended for use to minimize the effect of urban fire outbreak.