Compelling evidence shows the association of inflammation with atherosclerosis diseases, one of the leading cause of mortality and morbidity worldwide. Recent research indicated that the inflammatory process of atherosclerotic lesions is involved in the progression of atherosclerotic plaques in specific regions, such as the carotid bifurcation, which represents a risk for ischemic stroke as a result of the interaction between the blood and the plaque. We start modeling using 3D idealized geometry in order to capture the most important features of such interactions. Then, we proceed to a partly patient-specific computational domain representing an atherosclerotic artery. Understanding such interactions is of paramount importance preventing the risk of the plaque rupture. The numerical results comparisons have shown that, qualitatively, there is an agreement between idealized atherosclerotic artery and patient-specific atherosclerotic carotid artery. The idealized carotid geometry will be useful in future FSI studies of hemodynamic indicators based on medical images.
{"title":"A numerical 3D fluid-structure interaction model for blood flow in an atherosclerotic carotid artery","authors":"O. Kafi","doi":"10.23939/mmc2023.03.825","DOIUrl":"https://doi.org/10.23939/mmc2023.03.825","url":null,"abstract":"Compelling evidence shows the association of inflammation with atherosclerosis diseases, one of the leading cause of mortality and morbidity worldwide. Recent research indicated that the inflammatory process of atherosclerotic lesions is involved in the progression of atherosclerotic plaques in specific regions, such as the carotid bifurcation, which represents a risk for ischemic stroke as a result of the interaction between the blood and the plaque. We start modeling using 3D idealized geometry in order to capture the most important features of such interactions. Then, we proceed to a partly patient-specific computational domain representing an atherosclerotic artery. Understanding such interactions is of paramount importance preventing the risk of the plaque rupture. The numerical results comparisons have shown that, qualitatively, there is an agreement between idealized atherosclerotic artery and patient-specific atherosclerotic carotid artery. The idealized carotid geometry will be useful in future FSI studies of hemodynamic indicators based on medical images.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68769424","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}
A. P. Dzyuba, I. A. Safronova, V. N. Sirenko, A. R. Torskyy
The method of weight optimization of a shell structure consisting of a power ring frame connected to it on each side of non-homogeneous shells of rotation with variable wall thickness under the action of a spatially asymmetric load is presented. The construction decomposition algorithm is applied. The optimization of shells is carried out based on the necessary Pontryagin's optimality conditions with phase constraints. Finite-dimensional optimization methods are used to seek the optimal configuration of the ring frame. The synthesis of the construction is carried out by the method of successive approximations. Numerical optimization results are presented
{"title":"Parameter optimization decomposition and synthesis algorithm for a bundle of rotation shells connected with a ring frame","authors":"A. P. Dzyuba, I. A. Safronova, V. N. Sirenko, A. R. Torskyy","doi":"10.23939/mmc2023.03.976","DOIUrl":"https://doi.org/10.23939/mmc2023.03.976","url":null,"abstract":"The method of weight optimization of a shell structure consisting of a power ring frame connected to it on each side of non-homogeneous shells of rotation with variable wall thickness under the action of a spatially asymmetric load is presented. The construction decomposition algorithm is applied. The optimization of shells is carried out based on the necessary Pontryagin's optimality conditions with phase constraints. Finite-dimensional optimization methods are used to seek the optimal configuration of the ring frame. The synthesis of the construction is carried out by the method of successive approximations. Numerical optimization results are presented","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135799476","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}
An application of fractional Brownian motion (fBm) is considered in stochastic financial engineering models. For the known Fokker–Planck equation for the fBm case, a solution for transition probability density for the path integral method was built. It is shown that the mentioned solution does not result from the Gaussian unit of fBm with precise covariance. An expression for approximation of fBm covariance was found for which solutions are found based on the Gaussian measure of fBm and those found based on the known Fokker–Planck equation match.
{"title":"Fractional Brownian motion in financial engineering models","authors":"V. Yanishevskyi, L. Nodzhak","doi":"10.23939/mmc2023.02.445","DOIUrl":"https://doi.org/10.23939/mmc2023.02.445","url":null,"abstract":"An application of fractional Brownian motion (fBm) is considered in stochastic financial engineering models. For the known Fokker–Planck equation for the fBm case, a solution for transition probability density for the path integral method was built. It is shown that the mentioned solution does not result from the Gaussian unit of fBm with precise covariance. An expression for approximation of fBm covariance was found for which solutions are found based on the Gaussian measure of fBm and those found based on the known Fokker–Planck equation match.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68768463","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}
K. Addou, M. Y. El Ghoumari, S. Achkdir, M. Azzouazi
Many food contamination incidents have occurred during the last decade which has proven the failure of the food supply chain management system to track the food, money, and information movement within the food supply chain. Many models have been established. This paper presents the design and implementation of the new model providing real-time data acquisition, monitoring, and storing on a tamper-proof blockchain of the main food supply movement. This system is using smart contracts that are deployed on the Ethereum blockchain to allow every participant to transact securely with other FSC players. IoT networks are implemented in different workplaces to gather multiple data about food status without human involvement to ensure transparency by different sensors. Machine learning models are established to ensure the correctness of the collected data and help drive decision making within the application or businesses.
{"title":"A decentralized model to ensure traceability and sustainability of the food supply chain by combining blockchain, IoT, and machine learning","authors":"K. Addou, M. Y. El Ghoumari, S. Achkdir, M. Azzouazi","doi":"10.23939/mmc2023.02.498","DOIUrl":"https://doi.org/10.23939/mmc2023.02.498","url":null,"abstract":"Many food contamination incidents have occurred during the last decade which has proven the failure of the food supply chain management system to track the food, money, and information movement within the food supply chain. Many models have been established. This paper presents the design and implementation of the new model providing real-time data acquisition, monitoring, and storing on a tamper-proof blockchain of the main food supply movement. This system is using smart contracts that are deployed on the Ethereum blockchain to allow every participant to transact securely with other FSC players. IoT networks are implemented in different workplaces to gather multiple data about food status without human involvement to ensure transparency by different sensors. Machine learning models are established to ensure the correctness of the collected data and help drive decision making within the application or businesses.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68768533","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}
The dynamics of prey–predator interactions are often modeled using differential or difference equations. In this paper, we investigate the dynamical behavior of a two-dimensional discrete prey–predator system. The model is formulated in terms of difference equations and derived by using a nonstandard finite difference scheme (NSFD), which takes into consideration the non-overlapping generations. The existence of fixed points as well as their local asymptotic stability are proved. Further, it is shown that the model experiences Neimark–Sacker bifurcation (NSB for short) and period-doubling bifurcation (PDB) in a small neighborhood of the unique positive fixed point under certain parametric conditions. This analysis utilizes bifurcation theory and the center manifold theorem. The chaos produced by NSB and PDB is stabilized. Finally, we use numerical simulations and computer analysis to check our theories and show more complex behaviors.
{"title":"Complex dynamics and chaos control in a nonlinear discrete prey–predator model","authors":"K. Mokni, H. Ben Ali, M. Ch-Chaoui","doi":"10.23939/mmc2023.02.593","DOIUrl":"https://doi.org/10.23939/mmc2023.02.593","url":null,"abstract":"The dynamics of prey–predator interactions are often modeled using differential or difference equations. In this paper, we investigate the dynamical behavior of a two-dimensional discrete prey–predator system. The model is formulated in terms of difference equations and derived by using a nonstandard finite difference scheme (NSFD), which takes into consideration the non-overlapping generations. The existence of fixed points as well as their local asymptotic stability are proved. Further, it is shown that the model experiences Neimark–Sacker bifurcation (NSB for short) and period-doubling bifurcation (PDB) in a small neighborhood of the unique positive fixed point under certain parametric conditions. This analysis utilizes bifurcation theory and the center manifold theorem. The chaos produced by NSB and PDB is stabilized. Finally, we use numerical simulations and computer analysis to check our theories and show more complex behaviors.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68768839","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}
In this paper we develop and generalize the estimator of regression function for surrogate scalar response variable given a functional random one. Then, we build up some asymptotic properties in terms of the almost complete convergences, depending in the result we show the superiority of our estimator in term of prediction.
{"title":"Generalized regression function for surrogate scalar response","authors":"M. Boumahdi, I. Ouassou, Mustapha Rachdi","doi":"10.23939/mmc2023.03.625","DOIUrl":"https://doi.org/10.23939/mmc2023.03.625","url":null,"abstract":"In this paper we develop and generalize the estimator of regression function for surrogate scalar response variable given a functional random one. Then, we build up some asymptotic properties in terms of the almost complete convergences, depending in the result we show the superiority of our estimator in term of prediction.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68768869","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}
Due to advanced sensor technology, satellites and unmanned aerial vehicles (UAV) are producing a huge amount of data allowing advancement in all different kinds of earth observation applications. Thanks to this source of information, and driven by climate change concerns, renewable energy assessment became an increasing necessity among researchers and companies. Solar power, going from household rooftops to utility-scale farms, is reshaping the energy markets around the globe. However, the automatic identification of photovoltaic (PV) panels and solar farms' status is still an open question that, if answered properly, will help gauge solar power development and fulfill energy demands. Recently deep learning (DL) methods proved to be suitable to deal with remotely sensed data, hence allowing many opportunities to push further research regarding solar energy assessment. The coordination between the availability of remotely sensed data and the computer vision capabilities of deep learning has enabled researchers to provide possible solutions to the global mapping of solar farms and residential photovoltaic panels. However, the scores obtained by previous studies are questionable when it comes to dealing with the scarcity of photovoltaic systems. In this paper, we closely highlight and investigate the potential of remote sensing-driven DL approaches to cope with solar energy assessment. Given that many works have been recently released addressing such a challenge, reviewing and discussing them, it is highly motivated to keep its sustainable progress in future contributions. Then, we present a quick study highlighting how semantic segmentation models can be biased and yield significantly higher scores when inference is not sufficient. We provide a simulation of a leading semantic segmentation architecture U-Net and achieve performance scores as high as 99.78%. Nevertheless, further improvements should be made to increase the model's capability to achieve real photovoltaic units.
{"title":"Deep learning for photovoltaic panels segmentation","authors":"K. Bouzaâchane, A. Darouichi, E. E. El Guarmah","doi":"10.23939/mmc2023.03.638","DOIUrl":"https://doi.org/10.23939/mmc2023.03.638","url":null,"abstract":"Due to advanced sensor technology, satellites and unmanned aerial vehicles (UAV) are producing a huge amount of data allowing advancement in all different kinds of earth observation applications. Thanks to this source of information, and driven by climate change concerns, renewable energy assessment became an increasing necessity among researchers and companies. Solar power, going from household rooftops to utility-scale farms, is reshaping the energy markets around the globe. However, the automatic identification of photovoltaic (PV) panels and solar farms' status is still an open question that, if answered properly, will help gauge solar power development and fulfill energy demands. Recently deep learning (DL) methods proved to be suitable to deal with remotely sensed data, hence allowing many opportunities to push further research regarding solar energy assessment. The coordination between the availability of remotely sensed data and the computer vision capabilities of deep learning has enabled researchers to provide possible solutions to the global mapping of solar farms and residential photovoltaic panels. However, the scores obtained by previous studies are questionable when it comes to dealing with the scarcity of photovoltaic systems. In this paper, we closely highlight and investigate the potential of remote sensing-driven DL approaches to cope with solar energy assessment. Given that many works have been recently released addressing such a challenge, reviewing and discussing them, it is highly motivated to keep its sustainable progress in future contributions. Then, we present a quick study highlighting how semantic segmentation models can be biased and yield significantly higher scores when inference is not sufficient. We provide a simulation of a leading semantic segmentation architecture U-Net and achieve performance scores as high as 99.78%. Nevertheless, further improvements should be made to increase the model's capability to achieve real photovoltaic units.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68768883","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}
A new approach to inpainting problems that combines domain decomposition methods (DDM) with deep neural networks (DNN) to solve partial differential equations (PDE) is presented. First, this article examines different existing and emerging approaches to inpainting while emphasizing their advantages and disadvantages in a unified framework. After that, we introduce an algorithm that highlights the combination of DDM and DNN techniques for solving PDEs of a proposed mathematical inpainting model. For this model, the modified approach that has been adopted uses the DNN method which is based on convolutional neural networks (CNN) to reduce the computational cost in our algorithm while maintaining accuracy. Finally, the experimental results show that our method significantly outperforms existing ones for high-resolution images in paint stains.
{"title":"Enhancing image inpainting through image decomposition and deep neural networks","authors":"K. Bellaj, M. Benmir, S. Boujena","doi":"10.23939/mmc2023.03.720","DOIUrl":"https://doi.org/10.23939/mmc2023.03.720","url":null,"abstract":"A new approach to inpainting problems that combines domain decomposition methods (DDM) with deep neural networks (DNN) to solve partial differential equations (PDE) is presented. First, this article examines different existing and emerging approaches to inpainting while emphasizing their advantages and disadvantages in a unified framework. After that, we introduce an algorithm that highlights the combination of DDM and DNN techniques for solving PDEs of a proposed mathematical inpainting model. For this model, the modified approach that has been adopted uses the DNN method which is based on convolutional neural networks (CNN) to reduce the computational cost in our algorithm while maintaining accuracy. Finally, the experimental results show that our method significantly outperforms existing ones for high-resolution images in paint stains.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68769021","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}
R. Musii, U. Zhydyk, I. Svidrak, V. Shynder, N. Morska
The fundamental relations of the quasi-static problem of thermoelasticity are written for a finite layered orthotropic cylindrical shell of an antisymmetric structure. Under convective heat transfer on the surfaces of this shell and under a linear dependence of temperature on the transverse coordinate, the basic system of equations for the integral characteristics of temperature is given. The method is proposed for solving the formulated problems of thermoelasticity and thermal conductivity, using the double finite integral Fourier transform with respect to the corresponding coordinates of the transformation and Laplace transform with respect to the time. The results of a numerical analysis of temperature, deflections, and stresses for the considered two-layer shell hinged at the edges under local heating by the initially specified temperature field are presented.
{"title":"Determination and analysis of the thermoelastic state of layered orthotropic cylindrical shells","authors":"R. Musii, U. Zhydyk, I. Svidrak, V. Shynder, N. Morska","doi":"10.23939/mmc2023.03.918","DOIUrl":"https://doi.org/10.23939/mmc2023.03.918","url":null,"abstract":"The fundamental relations of the quasi-static problem of thermoelasticity are written for a finite layered orthotropic cylindrical shell of an antisymmetric structure. Under convective heat transfer on the surfaces of this shell and under a linear dependence of temperature on the transverse coordinate, the basic system of equations for the integral characteristics of temperature is given. The method is proposed for solving the formulated problems of thermoelasticity and thermal conductivity, using the double finite integral Fourier transform with respect to the corresponding coordinates of the transformation and Laplace transform with respect to the time. The results of a numerical analysis of temperature, deflections, and stresses for the considered two-layer shell hinged at the edges under local heating by the initially specified temperature field are presented.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68769587","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}
This paper deals with a fractional optimal control problem model that describes the interactions between hepatitis B virus (HBV) with HBV DNA-containing capsids, liver cells (hepatocytes), and the cytotoxic T-cell immune response. Optimal controls represent the effectiveness of drug therapy in inhibiting viral production and preventing new infections. The optimality system is derived and solved numerically. Our results also show that optimal treatment strategies reduce viral load and increase the number of uninfected cells, which improves the patient's quality of life.
{"title":"Dynamics of a fractional optimal control HBV infection model with capsids and CTL immune response","authors":"M. Ait Ichou, M. Bachraoui, K. Hattaf, N. Yousfi","doi":"10.23939/mmc2023.01.239","DOIUrl":"https://doi.org/10.23939/mmc2023.01.239","url":null,"abstract":"This paper deals with a fractional optimal control problem model that describes the interactions between hepatitis B virus (HBV) with HBV DNA-containing capsids, liver cells (hepatocytes), and the cytotoxic T-cell immune response. Optimal controls represent the effectiveness of drug therapy in inhibiting viral production and preventing new infections. The optimality system is derived and solved numerically. Our results also show that optimal treatment strategies reduce viral load and increase the number of uninfected cells, which improves the patient's quality of life.","PeriodicalId":37156,"journal":{"name":"Mathematical Modeling and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535121","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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