Multiple Perturbed Collocation Tau Method for Solving Nonlinear Integro-Differential Equations

The purpose of the study was to investigate the numerical solution of non-linear Fredholm and Volterra integro-differential equations by the proposed method called Multiple Perturbed Collocation Tau Method (MPCTM). We assumed a perturbed approximate solution in terms of Chebyshev  polynomial basis function and then determined the derivatives of the perturbed approximate solution which are then substituted into the special classes of the problems considered. Thus, resulting into n-folds integration, the resulting equation is then collocated at equally spaced interior points and the unknown constants in the approximate solution are then obtained by Newton’s method which are then substituted back into the approximate solution.Illustrative examples are given to demonstrate the efficiency, computational cost and accuracy of the method. The results obtained with some numerical examples are compared favorable with some existing numerical methods in literature and with the exact solutions where they are known in closed form.Keywords: Nonlinear Problems, Tau Method, Integro-Differential, Newton’s method

On a Stable and Consistent Finite Difference Scheme for a Time-Dependent Schrodinger Wave Equation in a Finitely Low Potential Well

In this paper, a stable and consistent criterion to an explicit finite difference scheme for a time-dependent Schrodinger wave equation (TDSWE) was presented. This paper is a departure from the well-established time independent Schrodinger Wave Equation (SWE). To develop the stability criterion for the scheme, the Fourier series method of von Newmann was adopted, while in establishing the consistency property, the concise definition of the consistent scheme was applied. This research is carried out for a particular case of a finitely low potential well.Keywords: Time-Dependent Schrodinger Wave Equation, Stability, Consistency, Finite Potential Well, Finite Difference

Assessment of groundwater contamination by leachate near a municipal solid waste landfill

Physico-chemical and microbiological parameters were analyzed in leachate and groundwater samples obtained at different locations adjacent to a municipal solid waste landfill in order to assess the impact of leachate percolation on groundwater quality. Total dissolved solids (TDS), electrical conductivity (EC), and Na+ exceeded the World Health Organization (WHO) tolerance levels for drinking water in 62.5, 100, and 37.5% of the groundwater samples, respectively with pH and Fe exceeding WHO limits in 75% of the samples. Significant negative correlations of -0.839, -0.590, and -0.590 were shown by Na+, TDS, and EC respectively to distance from landfill. A high population of Enterobacteriaceae ranging from 4.0 × 103 ± 0 to 1.0575 × 106 ± 162,705 CFU/ml was also detected in the groundwater samples, indicating contamination. The results show that the leachate from the landfill has a minimal impact on the groundwater resource and this can be attributed to the existing soil stratigraphy at the site consisting of clay which is deduced to have a significant influence on the natural attenuation of leachate into groundwater.Keywords: Groundwater, correlation, percolation, landfill, leachate, municipal solid waste, natural attenuation, Enterobacteriaceae

An integrated Dynamic Pricing Scheme for improving the smartness of off grid distributed generation

This paper presents an integrated dynamic pricing scheme (iDPS) developed for an off grid community in sub-Sahara Africa (SSA). This proposed model employs a neighbourhood approach in effectively determining the electricity units due to connected consumers based on their monthly contributions. Assuming base values, this model obviates the need for real time inputs from the users owing to the high illiteracy level in SSA and dynamically computes electricity price in real time such that below par paying consumers pay more compared to base or above base consumers. Additionally, the smart load distribution board employed ensures that electricity units are dispatched in quantized values demanding greater energy efficiency from the consumers. This model achieves economic accrual which guarantees the sustainability of the off grid DG project

Automated scheduling of hostel room allocation using genetic algorithm

Due to the rapid growth of the student population in tertiary institutions in many developing countries, hostel space has become one of the most important resources in university. Therefore, the decision of student selection and hostel room allocation is indeed a critical issue for university administration. This paper proposes a hierarchical heuristics approach to cope with hostel room allocation problem. The proposed approach involves selecting eligible students using rank based selection method and allocating selected students to the most suitable hostel room possible via the implementation of a genetic algorithm (GA). We also have examined the effects of using different weight associated with constraints on the performance of the GA. Results obtained from the experiments illustrate the feasibility of the suggested approach in solving the hostel room allocation problem

Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem

Symbiotic Organisms Search (SOS) algorithm is an effective new metaheuristic search algorithm, which has recently recorded wider application in solving complex optimization problems. SOS mimics the symbiotic relationship strategies adopted by organisms in the ecosystem for survival. This paper, presents a study on the application of SOS with Simulated Annealing (SA) to solve the well-known traveling salesman problems (TSPs). The TSP is known to be NP-hard, which consist of a set of (n − 1)!/2 feasible solutions. The intent of the proposed hybrid method is to evaluate the convergence behaviour and scalability of the symbiotic organism’s search with simulated annealing to solve both small and large-scale travelling salesman problems. The implementation of the SA based SOS (SOS-SA) algorithm was done in the MATLAB environment. To inspect the performance of the proposed hybrid optimization method, experiments on the solution convergence, average execution time, and percentage deviations of both the best and average solutions to the best known solution were conducted. Similarly, in order to obtain unbiased and comprehensive comparisons, descriptive statistics such as mean, standard deviation, minimum, maximum and range were used to describe each of the algorithms, in the analysis section. The oneway ANOVA and Kruskal-Wallis test were further used to compare the significant difference in performance between SOS-SA and the other selected state-of-the-art algorithms. The performances of SOS-SA and SOS are evaluated on different sets of TSP benchmarks obtained from TSPLIB (a library containing samples of TSP instances). The empirical analysis’ results show that the quality of the final results as well as the convergence rate of the new algorithm in some cases produced even more superior solutions than the best known TSP benchmarked results

Integrating statistical machine learning in a semantic sensor web for proactive monitoring and control

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web

Human embryonic stem cells from aneuploid blastocysts identified by pre-implantation genetic screening

Human embryonic stem cells are derived from the inner cell mass of pre-implantation embryos. The cells have unlimited proliferation potential and capacity to differentiate into the cells of the three germ layers. Human embryonic stem cells are used to study human embryogenesis and disease modeling and may in the future serve as cells for cell therapy and drug screening. Human embryonic stem cells are usually isolated from surplus normal frozen embryos and were suggested to be isolated from diseased embryos detected by pre-implantation genetic diagnosis. Here we report the isolation of 12 human embryonic stem cell lines and their thorough characterization. The lines were derived from embryos detected to have aneuploidy by pre-implantation genetic screening. Karyotype analysis of these cell lines showed that they are euploid, having 46 chromosomes. Our interpretation is that the euploid cells originated from mosaic embryos, and in vitro selection favored the euploid cells. The undifferentiated cells exhibited long-term proliferation and expressed markers typical for embryonic stem cells such as OCT4, NANOG, and TRA-1-60. The cells manifested pluripotent differentiation both in vivo and in vitro. To further characterize the different lines, we have analyzed their ethnic origin and the family relatedness among them. The above results led us to conclude that the aneuploid mosaic embryos that are destined to be discarded can serve as source for normal euploid human embryonic stem cell lines. These lines represent various ethnic groups; more lines are needed to represent all populations

Effects of ethanolic extract of garlic, roselle and ginger on quality attributes of chicken patties

Efficiency of ethanolic extracts of garlic, ginger and roselle on quality attributes of chicken patties was investigated. Sensorial qualities were evaluated using a 9-point hedonic scale. Lipid oxidation was assessed by monitoring malondialdehyde formation with 2-thiobarbituric acid (TBARS) assay. Total plate count (log10CFU/g) and Warmed Over Flavour (WOF) were determined on days 0, 7 and 14 of refrigerated storage while proximate composition was determined on freshly prepared patties using a standard procedure. Sensory evaluation revealed high scores for Overall Acceptability (OA) of patties containing the plant extracts while the highest score of aroma was recorded in products with ginger extract. The nutrient composition of the products were not affected (P>0.05) by the plant extracts. The cost of production reduced by 1.81, 0.49, 2.75 and 0.53% with the addition of 0.05% of garlic, ginger, roselle extracts and α-tocopherol respectively in comparison to the negative control. The WOF formation reduced from 17.65 to 39.29% by the inclusion of the extracts. The microbial load also reduced in comparison to the negative control. In conclusion, the plant extracts used in this study provided antioxidant and antimicrobial benefits to raw chicken patties during cold storage (4°C). As herbs/spices, they could be used to extend the shelf-life of chicken patties and provide the consumer with food containing natural additives, which might be more healthful. Key words: Chicken patties, plant extracts, sensory attributes, lipid oxidation

Comparative physiological parameters in West African Dwarf and Yankasa Sheep

No AbstractBulletin of Animal Health and Production in Africa Vol. 55 (2) 2007: pp. 146-14