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Optimized Hybrid Block Adams Method for Solving First Order Ordinary Differential Equations

Computers, Materials & Continua 2022年第8期72卷 2947-2961页

作者： Hira Soomro nooraini zainuddin Hanita Daud Joshua Sunday Department of Fundamental and Applied Sciences Universiti Teknologi PETRONAS32610Seri IskandarPerakMalaysia Department of Mathematics University of Jos930003JosNigeria

Multistep integration methods are being extensively used in the simulations of high dimensional systems due to their lower computational cost.The block methods were developed with the intent of obtaining numerical results on numerous points at a time and improving computational efficiency.Hybrid block methods for instance are specifically used in numerical integration of initial value problems.In this paper,an optimized hybrid block Adams block method is designed for the solutions of linear and nonlinear first-order initial value problems in ordinary differential equations(ODEs).In deriving themethod,the Lagrange interpolation polynomial was employed based on some data points to replace the differential equation function and it was integrated over a specified interval.Furthermore,the convergence properties along with the region of stability of the method were examined.It was concluded that the newly derived method is convergent,consistent,and zero-stable.The method was also found to be A-stable implying that it covers the whole of the left/negative half plane.From the numerical computations of absolute errors carried out using the newly derived method,it was found that the method performed better than the ones with which we compared our results with.Themethod also showed its superiority over the existing methods in terms of stability and convergence.

关键词： Initial value problem(IVPs) linear multi-step method block interpolation hybrid Adams-Moulton method

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An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data

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Computers, Materials & Continua 2022年第1期70卷 1945-1953页

作者： Sami Ullah Nurul Hidayah Mohd Nor Hanita Daud nooraini zainuddin Hadi Fanaee-T Alamgir Khalil Department of Fundamental&Applied Sciences Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Center for Applied Intelligent Systems Research(CAISR) Halmstad UniversityHalmstadSE-30118Sweden Department of Statistics University of PeshawarPakistan

Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies.The state-of-the-art method for this kind of problem is the Space-time Scan Statistics(SaTScan)which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson orGaussian counts.Addressing this problem,an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution.However,it is based on the population counts data which are not always available in the least developed countries.In addition,the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales,where the catchment area for each hospital/pharmacy is undefined.We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts.The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts.The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods:Multi-EigenSpot and SaTScan.The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.

关键词： Space-time disease clusters Eigenspace method nontraditional data sources nonparametric methods

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Space-Time Cluster Analysis of Accidental Oil Spills in Rivers State, Nigeria, 2011–2019

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Computers, Materials & Continua 2021年第3期66卷 3065-3074页

作者： Sami Ullah Hanita Daud nooraini zainuddin Sarat CDass Alamgir Khalil Hadi Fanaee-T Ilyas Khan Department of Fundamental&Applied Sciences Universiti Teknologi PETRONAS32610Seri IskandarPerakMalaysia School of Mathematical and Computer Sciences Heriot-Watt University Malaysia62200PutrajayaMalaysia Department of Statistics University of PeshawarPakistan Center for Applied Intelligent Systems Research(CAISR) Halmstad UniversityHalmstadSweden Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City72915Vietnam

Oil spills cause environmental pollution with a serious threat to local communities and sustainable development.Accidental oil spills can be modelled as a stochastic process where each oil spill event is described by its spatial locations and incidence-time and hence allow for space-time cluster analysis.Spacetime cluster analysis can detect space-time pattern distribution of oil spills which can be useful for implementing preventive measures and evidence-based decision making.This study aims to detect the space-time clusters of accidental oil spills in Rivers state,Nigeria through the Space-time Scan Statistic.The Space-time Scan Statistic was applied under the permutation model to the oil spill data(each for sabotage and operational oil spills)collected at Local Government Area(LGA)-level during the period from 2011 to 2019.The results show that the sabotage oil spill clusters have covered most of the LGAs in the southern part of the state at the start of the study period and then in 2018–2019,it moved to the west covering a single LGA.The operational oil spill clusters covered two neighboring LGAs in the south.In addition,the temporal cluster of sabotage oil spills was seen in 2019 and operational oil spills in 2011–2012.The sabotage oil spills show an increasing trend with the maximum in 2019 while the operational oil spills show a decreasing trend with the minimum in 2019.These findings assist in more effective decision-making for combating the environmental problems and controlling the future spill incidence in the cluster-regions.

关键词： Oil spills space-time clusters SaTScan^(™) permutation model Nigeria

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