Effect of Processing Parameters on Solvent Oil Expression from Loofah Seeds (Luffa cylindrica L.) using Response Surface Methodology

Luffah cylindrica plant grows in the wild, around uncompleted buildings and fenced walls. The percentage oil composition of its seeds is about 30% oil. The research focused was the extraction oil from loofah seed using a solvent extraction methodology. Optimum conditions for oil extraction were determined using Response Surface Methodology of Central Composite Rotatable Design. A total of 20 experimental runs were used to investigate the optimum condition considering three independent variables at five levels each: extraction temperature (55, 60, 65, 60, 75ºC), seed/solvent ratio (0.04, 0.05, 0.06, 0.07, 0.08 g/ml) and extraction time (4, 5, 6, 7, 8 hr.). An empirical model equation that could be used to forecast oil yield as a function of the independent variables was developed. The optimum oil yield obtained was 27.43% at the extraction temperature (74.05ºC), seed/solvent ratio (0.05 g/ml) and extraction time (5.35hr). The analysis of variance showed that extraction temperature and time had significant effect on oil yield (p = 0.05). The interaction of the independent variables with oil yield gave R2 and R2 adj. values of 0.98 and 0.93, respectively. The result showed that the selected independent variables had a significant effect on oil yield, thus an optimum condition was established

Automated classification of African embroidery patterns using cellular learning automata and support vector machines

Embroidery is the art that is majorly practised in Nigeria, which requires creativity and skills. However, differentiating between two standard embroidery patterns pose challenges to wearers of the patterns. This study developed a classification system to improve the embroiderer to user relationship. The specific characteristics are used as feature sets to classify two common African embroidery patterns (handmade and tinko) are shape, brightness, thickness and colour. The system developed and simulated in MATLAB 2016a environment employed Cellular Learning Automata (CLA) and Support Vector Machine (SVM) as its classifier. The classification performance of the proposed system was evaluated using precision, recall, and accuracy. The system obtained an average precision of 0.93, average recall of 0.81, and average accuracy of 0.97 in classifying the handmade and tinko embroidery patterns considered in this study. This study also presented an experimental result of three validation models for training and testing the dataset used in this study. The model developed an improved and refined embroiderer for eliminating stress related to the manual pattern identification process

Computational Modelling of Collaborative Resources Sharing in Grid System

In grid computing, Grid users who submit jobs or tasks and resources providers who provide resources have different motivations when they join the Grid system. However, due to autonomy both the Grid users' and resource providers' objectives often conflict. This paper proposes autonomous hybrid resource management algorithm for optimizing the resource utilization of resources providers using “what-yougive-is-what-you-get” Service Level Agreements resource allocation policy. Utility functions are used to achieve the objectives of Grid resource and application. The algorithm was formulated as joint optimization of utilities of Grid applications and Grid resources, which combines the resource contributed, incentive score, trustworthiness and reputation score to compute resource utilization. Simulations were conducted to study the performance of the algorithm using GridSim v5.0. The simulation results revealed that the algorithm yields significantly good result because no user can consume more than what it contributes under different scenarios; hence the problem of free riding has been addressed through this algorithm.Keywords: Resource scheduling, Grid System, Computational modellin