Pub Date : 2023-04-01DOI: 10.13189/ujam.2023.110102
Mário Basto, Teresa Abreu, R. Gonçalves, J. M. Pereira
{"title":"Confounding and Effect Modification in Biostatistics: An Overview","authors":"Mário Basto, Teresa Abreu, R. Gonçalves, J. M. Pereira","doi":"10.13189/ujam.2023.110102","DOIUrl":"https://doi.org/10.13189/ujam.2023.110102","url":null,"abstract":"","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121543646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.13189/ujam.2022.100301
H. Phan, Seonguk Kim
{"title":"Numerical Approaches of Pricing European Options in the Cox-Ross-Rubinstein Models","authors":"H. Phan, Seonguk Kim","doi":"10.13189/ujam.2022.100301","DOIUrl":"https://doi.org/10.13189/ujam.2022.100301","url":null,"abstract":"","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128286295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.13189/ujms.2022.100203
Madhusudan V Atre, Hrvoje Volarević
{"title":"Modelling J-Curve by Using Differential Equations: Studies in Finances and Entrepreneurship","authors":"Madhusudan V Atre, Hrvoje Volarević","doi":"10.13189/ujms.2022.100203","DOIUrl":"https://doi.org/10.13189/ujms.2022.100203","url":null,"abstract":"","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"90 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129833527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.13189/ujam.2022.100201
Aydin Teymourifar, Jie Li, Dan Li, Taicheng Zheng
{"title":"An Open-Source Simulation Model for Solving Scheduling Problems","authors":"Aydin Teymourifar, Jie Li, Dan Li, Taicheng Zheng","doi":"10.13189/ujam.2022.100201","DOIUrl":"https://doi.org/10.13189/ujam.2022.100201","url":null,"abstract":"","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121088234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.13189/ujam.2022.100101
M. S. Sinaga, H. D. M. Hutabarat, Y. Rangkuti
{"title":"New Normal Destination Development in the Tourism Industry","authors":"M. S. Sinaga, H. D. M. Hutabarat, Y. Rangkuti","doi":"10.13189/ujam.2022.100101","DOIUrl":"https://doi.org/10.13189/ujam.2022.100101","url":null,"abstract":"","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124457314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.13189/ujam.2021.090201
Aydin Teymourifar
Artificial neural networks (ANNs) have been used for estimation in numerous areas. Raising the accuracy of ANNs is always one of the important challenges, which is generally defined as a non-linear optimization problem. The aim of this optimization is to find better values for the weights of the connections and biases in ANN because they seriously affect the efficiency. This study uses two approaches to do such optimization in an ANN. For this aim, we create a feed-forward backpropagation ANN using the functions of MATLAB's deep learning toolbox. To improve its accuracy, in the first approach, we use the Levenberg - Marquardt algorithm (LMA) for training, which is available in MATLAB's deep learning toolbox. In the second approach, we optimize the values of weights and biases of ANN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), available in MATLAB's global optimization toolbox. Then, we assess the accuracy of estimation for the trained ANNs. In this way, for the first time in the literature, we compare these methods for the optimization of an ANN. The used data sets are also available in MATLAB. Based on the acquired results, in some data sets, training with LMA, and for some others training with PSO cause the best results, however, training with LMA is faster, significantly. Although the used approaches and the obtained conclusions are beneficial for researchers that work in this field, they have some limitations. For instance, since only the functions and data sets from MATLAB are used, it can only serve as an example for researchers.
{"title":"A Comparison Between Two Approaches to Optimize Weights of Connections in Artificial Neural Networks","authors":"Aydin Teymourifar","doi":"10.13189/ujam.2021.090201","DOIUrl":"https://doi.org/10.13189/ujam.2021.090201","url":null,"abstract":"Artificial neural networks (ANNs) have been used for estimation in numerous areas. Raising the accuracy of ANNs is always one of the important challenges, which is generally defined as a non-linear optimization problem. The aim of this optimization is to find better values for the weights of the connections and biases in ANN because they seriously affect the efficiency. This study uses two approaches to do such optimization in an ANN. For this aim, we create a feed-forward backpropagation ANN using the functions of MATLAB's deep learning toolbox. To improve its accuracy, in the first approach, we use the Levenberg - Marquardt algorithm (LMA) for training, which is available in MATLAB's deep learning toolbox. In the second approach, we optimize the values of weights and biases of ANN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), available in MATLAB's global optimization toolbox. Then, we assess the accuracy of estimation for the trained ANNs. In this way, for the first time in the literature, we compare these methods for the optimization of an ANN. The used data sets are also available in MATLAB. Based on the acquired results, in some data sets, training with LMA, and for some others training with PSO cause the best results, however, training with LMA is faster, significantly. Although the used approaches and the obtained conclusions are beneficial for researchers that work in this field, they have some limitations. For instance, since only the functions and data sets from MATLAB are used, it can only serve as an example for researchers.","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"18 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125735605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-01DOI: 10.13189/UJAM.2021.090102
A. Wusu, O. Olabanjo, Benjamin S. Aribisala
The first case of the novel coronavirus (COVID–19) in sub–Saharan Africa was confirmed by Nigeria and the figure has since then been on the rise. Current global efforts are geared towards getting effective vaccine for the cure of the disease. The hope of accessing the relieve offered by the arrival of such vaccine will obviously take significant amount of t ime. In the face of the resurgence of the disease, the need to slow the spread and flatten the curves is currently a priority of both governmental and non–governmental organisations in Nigeria. If the dynamics of the disease can be determined, then it becomes easier to strategize and make suitable preventive policies that will slow the spread and ultimately flatten the c urves. Here, the goal is to develop a compartmental–based model for analysing the dynamics of the pandemic in Nigeria. Considering the control policies currently in place social distancing, mask usage, personal hygiene and quarantine, and using data provided by Nigeria Centre for Disease Control (NCDC), World Health Organization (WHO) and Wolfram Data Repository on COVID–19, the proposed model is fitted to the available data using the Quasi-Newton algorithm. The infection rate, average latent time, average infective time and average mortality rate are estimated. Also, the overall effectiveness of the current control policies is measured. Predictions on the turning points and possible vanishing time of the virus in Nigeria are made. Recommendations on how to manage the resurgence of the disease in Nigeria are also suggested.
{"title":"A SEIRD Model for Analysing the Dynamics of Coronavirus (COVID-19) Pandemic in Nigeria","authors":"A. Wusu, O. Olabanjo, Benjamin S. Aribisala","doi":"10.13189/UJAM.2021.090102","DOIUrl":"https://doi.org/10.13189/UJAM.2021.090102","url":null,"abstract":"The first case of the novel coronavirus (COVID–19) in sub–Saharan Africa was confirmed by Nigeria and the figure has since then been on the rise. Current global efforts are geared towards getting effective vaccine for the cure of the disease. The hope of accessing the relieve offered by the arrival of such vaccine will obviously take significant amount of t ime. In the face of the resurgence of the disease, the need to slow the spread and flatten the curves is currently a priority of both governmental and non–governmental organisations in Nigeria. If the dynamics of the disease can be determined, then it becomes easier to strategize and make suitable preventive policies that will slow the spread and ultimately flatten the c urves. Here, the goal is to develop a compartmental–based model for analysing the dynamics of the pandemic in Nigeria. Considering the control policies currently in place social distancing, mask usage, personal hygiene and quarantine, and using data provided by Nigeria Centre for Disease Control (NCDC), World Health Organization (WHO) and Wolfram Data Repository on COVID–19, the proposed model is fitted to the available data using the Quasi-Newton algorithm. The infection rate, average latent time, average infective time and average mortality rate are estimated. Also, the overall effectiveness of the current control policies is measured. Predictions on the turning points and possible vanishing time of the virus in Nigeria are made. Recommendations on how to manage the resurgence of the disease in Nigeria are also suggested.","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124107954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.13189/ujam.2020.080201
S. Tomita, Oliver Couto
Consider the below mentioned Equation: ax4+by4+cz4+dw4=0---[1]. In section (1) we consider solution's with the condition on the coefficient's of equation[1]. Namely the product (abcd)=square. In section [2] we consider the coefficients of Equation [1], with the product of coefficient's (abcd) not equal to a square. Historically Equation [1] has been studied by Ajai Choudhry, A. Bremner, M.Ulas [ref. 5] in 2014. Also Richmond [ref. 1 & 2] has done some ground work in 1944 & 1948. This paper has gone a step further, by finding many parametric solutions & new small numerical solutions by the use of unique Identities. The identities are unique, because they are of mixed powers (combination of quartic & quadratic variables) which are then converted to only degree four identities. As an added bonus in section [B], we came up with a few quartic (4-1-n ) numerical solutions for (n < 50) by elliptical mean's. A table of numerical solutions for the (4-1-n) Equation arrived at by brute force computer search is also given [ref # 7].
{"title":"Equal Sums of Quartics (In Context with the Richmond Equation) (ax4+by4+cz4+dw4=0)","authors":"S. Tomita, Oliver Couto","doi":"10.13189/ujam.2020.080201","DOIUrl":"https://doi.org/10.13189/ujam.2020.080201","url":null,"abstract":"Consider the below mentioned Equation: ax4+by4+cz4+dw4=0---[1]. In section (1) we consider solution's with the condition on the coefficient's of equation[1]. Namely the product (abcd)=square. In section [2] we consider the coefficients of Equation [1], with the product of coefficient's (abcd) not equal to a square. Historically Equation [1] has been studied by Ajai Choudhry, A. Bremner, M.Ulas [ref. 5] in 2014. Also Richmond [ref. 1 & 2] has done some ground work in 1944 & 1948. This paper has gone a step further, by finding many parametric solutions & new small numerical solutions by the use of unique Identities. The identities are unique, because they are of mixed powers (combination of quartic & quadratic variables) which are then converted to only degree four identities. As an added bonus in section [B], we came up with a few quartic (4-1-n ) numerical solutions for (n < 50) by elliptical mean's. A table of numerical solutions for the (4-1-n) Equation arrived at by brute force computer search is also given [ref # 7].","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"48 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116208627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.13189/ujam.2020.080101
D. Demir, Gülsüm Şanal
The aim of this study is to present some inequalities for n-times differentiable functions. These inequalities are associated with the perturbed trapezoid inequality. n th derivatives of absolute values of the considered functions are s-convex and tgs-convex. In previous studies, some inequalities for the classes of twice differentiable functions which are convex, s-convex and tgs-convex have been introduced. We expand twice differentiable functions to n-times differentiable functions. Finally, some applications are given to verify new inequalities proposed in this study.
{"title":"Some Inequalities for N-Times Differentiable s-Convex and tgs-Convex Functions","authors":"D. Demir, Gülsüm Şanal","doi":"10.13189/ujam.2020.080101","DOIUrl":"https://doi.org/10.13189/ujam.2020.080101","url":null,"abstract":"The aim of this study is to present some inequalities for n-times differentiable functions. These inequalities are associated with the perturbed trapezoid inequality. n th derivatives of absolute values of the considered functions are s-convex and tgs-convex. In previous studies, some inequalities for the classes of twice differentiable functions which are convex, s-convex and tgs-convex have been introduced. We expand twice differentiable functions to n-times differentiable functions. Finally, some applications are given to verify new inequalities proposed in this study.","PeriodicalId":372283,"journal":{"name":"Universal Journal of Applied Mathematics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116469800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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