Water loss through leaking pipes constitutes a major challenge to the operational service of water utilities. In recent years, increasing concern about the financial loss and environmental pollution caused by leaking pipes has been driving the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs) are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s) of the network and the exact leaking pipelines connected to this segment(s) where higher background leakage outflow occurs is a challenging task. Background leakage concerns the outflow from small cracks or deteriorated joints. In addition, because they are diffuse flow, they are not characterised by quick pressure drop and are not detectable by measuring instruments. Consequently, they go unreported for a long period of time posing a threat to water loss volume. Most of the existing research focuses on the detection and localisation of burst type leakages which are characterised by a sudden pressure drop. In this work, an algorithm for detecting and estimating background leakage in water distribution networks is presented. The algorithm integrates a leakage model into a classical WDN hydraulic model for solving the network leakage flows. The applicability of the developed algorithm is demonstrated on two different water networks. The results of the tested networks are discussed and the solutions obtained show the benefits of the proposed algorithm. A noteworthy evidence is that the algorithm permits the detection of critical segments or pipes of the network experiencing higher leakage outflow and indicates the probable pipes of the network where pressure control can be performed. However, the possible position of pressure control elements along such critical pipes will be addressed in future work.

This study was conducted to investigate the synthesis, characterization and mechanical properties of aluminium reinforced with ferrotitanium and silicon carbide via stir casting technique. Microstructures of as-cast samples were analysed using optical and scanning electron microscopes equipped with energy-dispersive X-ray spectroscopy. The mechanical properties in terms of hardness, tensile, tribological behaviour and fracture were assessed. Results showed that the homogeneous dispersion of reinforcement was within the metal matrix composite. Tribological study revealed a decrease in frictional coefficient of the composites with lowest frictional coefficient observed in composite with addition of silicon carbide as reinforcement. Morphology of fractured surface displayed a reduction in the size of dimples formed in reinforced aluminium composites when compared with larger dimple sizes observed in as-cast aluminium alloy.

The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.

It is well known that existing empirical models cannot fit perfectly into environments other than those they were formulated in due to differences in terrain and climate. The Okumura–Hata family of models are gaining acceptability over the VHF/UHF channels. However, it is imperative to investigate their reliability and to use the one most suited to each environment. This study investigated the reliability of the Okumura–Hata, COST-231, ECC-33, and Ericsson models over digital UHF channels in Ikorodu and Akure, Southwestern Nigeria. The drive test protocol was used for data collection at intervals of 1 km along different routes from the experimental stations up to maximums of 10 and 16 km in Ikorodu and Akure, respectively. This was carried out for both wet and dry season months using a digital Satlink meter with a spectrum (WS-6936), GPS Map 78s and a field vehicle. The uniqueness of this study is that it used real-world data with a seasonal scope, and the mean values were employed in the analysis to strengthen the reliability of the results. The measured path loss (MPL) and predicted path loss (PPLM) were computed, with error margin analysis carried out between them. The results reveal a mean MPL of 110.42 dB in Ikorodu, while the PPLMs were 121.90, 123.55, 158.42, and 291.01 dB for the Hata, COST-231, Ericsson, and ECC-33 models, respectively. In Akure, the mean MPL was 123.157 dB, while the PPLMs were 121.922, 130.179, 198.979, and 313.494 dB. The results further indicate that the Hata model had the best performance with the lowest RMSE of 10.812 in Ikorodu, while COST-231 had the best performance in Akure, with the lowest RMSE of 9.877. The optimized Hata and COST-231 models were developed with improved RMSEs of 5.895 and 7.815 for the Ikorodu and Akure environments, respectively. The optimized models had higher degrees of reliability and will provide a valuable approach to wireless communication planning in tropical urban and suburban environments for achieving quality of transmission and reception (QoTnR) over UHF channels in Nigeria and similar environments in Africa.

The limitations of aluminium in most engineering applications has led to the development of aluminium matrix composites with improved microstructural and mechanical properties. Nanoindentation techniques was used in assessing the mechanical properties of fabricated aluminium matrix composites with ferrotitanium and silicon carbide as reinforcements. Results from nanoindentation experiments shows the dependence of modulus of elasticity, microhardness and contact depth on the dispersion of ferrotitanium and silicon carbide reinforcements within the aluminium matrix. Highest nanohardness value was observed in composite with 7 wt. % silicon carbide, while the lowest elastic modulus was recorded in as-cast aluminium. Further analysis of specimens confirmed a decrease in maximum penetration depth with respective increase in the addition of silicon carbide reinforcements in the fabricated composites.