The present study was designed to estimate the substantial equivalence of genetically modified (G... more The present study was designed to estimate the substantial equivalence of genetically modified (GM) potato Spunta lines (SpG2, SpG3 and Sp6A-3) with the cry1Ic1 gene compared to conventional non-transgenic potato Spunta cultivar (control group) through rat feeding experiments and through the determination of nutritional composition such as: protein, ash, fiber, fat, starch, total carbohydrates, amino acids, fatty acids, micro and macroelements and vitamins B1, B2 and C. Short-term feeding study was conducted for nine days using experimental rats to estimate the digestibility parameters: True Digestibility, (T.D.), Biological Value (B.V.) and Net Protein Utilization (N.P.U.) as well as the behavior of rats under investigation. Rats in each group (SpG2, SpG3 and Sp6A-3 and control groups) grew normally without marked differences in appearance, behavior or in mortality rate. No marked differences were observed in the T.D., B.V. and N.P.U. in all groups. All tested chemical parameters s...
Abstract Chemical process operation optimization aims at obtaining the optimal operating set-poin... more Abstract Chemical process operation optimization aims at obtaining the optimal operating set-points by real-time solution of an optimization problem that embeds a steady-state model of the process. This task is challenged by unavoidable Uncertain Parameters (UPs) variations. MultiParametric Programming (MPP) is an approach for solving this challenge, where the optimal set-points must be updated online, reacting to sudden changes in the UPs. MPP provides algebraic functions describing the optimal solution as a function of the UPs, which allows alleviating large computational cost required for solving the optimization problem each time the UPs values vary. However, MPP applicability requires a well-constructed mathematical model of the process, which is not suited for process operation optimization, where complex, highly nonlinear and/or black-box models are usually used. To tackle this issue, this paper proposes a machine learning-based methodology for multiparametric solution of continuous optimization problems. The methodology relies on the offline development of data-driven models that accurately approximate the multiparametric behavior of the optimal solution over the UPs space. The models are developed using data generated by running the optimization using the original complex process model under different UPs values. The models are, then, used online to, quickly, predict the optimal solutions in response to UPs variation. The methodology is applied to benchmark examples and two case studies of process operation optimization. The results demonstrate the methodology effectiveness in terms of high prediction accuracy (less than 1% of NRMSE, in most cases), robustness to deal with problems of different natures (linear, bilinear, quadratic, nonlinear and/or black boxes) and significant reduction in the complexity of the solution procedure compared to traditional approaches (a minimum of 67% reduction in the optimization time).
Abstract The complexity of optimization problems of optimization problems increases along with th... more Abstract The complexity of optimization problems of optimization problems increases along with the dimensions, non-linearity, and/or the required accuracy of the model constraints and objective functions. Additionally, for mixed-integer multiparametric problems, the discreet and uncertain nature of the variables and parameters to be considered, affect the complexity further more. Recently, machine learning or data-driven techniques have been proposed as alternatives for the solution of complex multiparametric programming problems. However, those methods presents as a main limitation its very high prediction error in variables that show discrete behavior and on the limits of the critical/local regions. This work extends this investigation line via proposing a novel machine learning method for solving these kind of problems based on an iterative process that use Ordinary Kriging as supervised learning tool to classify and model data. Furthermore, Ordinary Kriging can be also used, as an unsupervised tool to cluster data. The proposed methodology is applied to a benchmark case-study and the numerical results exhibits a significant improvements, up to 65% based on the normalized root-mean-square error, compared with reported information that used other modeling techniques.
This paper investigates the extension of a MultiParametric approach based on surrogate models (Me... more This paper investigates the extension of a MultiParametric approach based on surrogate models (Meta-MultiParametric approach, M-MP) in order to handle general Mixed- Integer (MI) optimization problems involving Uncertain Parameters (UPs). The method harnesses metamodeling and clustering techniques in order to approximate black box relations between the optimal values of the continuous variables and the UPs, while Classification Techniques (CT) are employed to identify the optimal values of the integer variables also as a function of the UPs. The results of applying the method to a benchmark case-study show a high prediction accuracy of the optimal solutions, saving computational effort and overpassing the complex mathematical procedures required by the standard MultiParametric Programming methods.
Abstract This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiS... more Abstract This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiStep-Ahead Prediction (MSAP) of nonlinear systems based on the Ordinary Kriging (OK) metamodel. The OK is used to build a set of Nonlinear Autoregressive models with Exogenous inputs (NAREX), able to approximate the system future outputs as a function of previous inputs and outputs considering a specific delay. Then, these OK-based dynamic models are used in a recursive interactive interpolations scheme to predict the process outputs over several time steps. The capabilities of the OK-based dynamic models are compared to other leading techniques, via their application to benchmark cases. The application results reveal the OK promising and competitive capabilities for MVDM of nonlinear systems, in terms of prediction accuracy and prediction time horizon.
Abstract The digitalization of nuclear power plants, with the rapid growth of information technol... more Abstract The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.
Journal of the Egyptian National Cancer Institute, 2018
Osteosarcoma (OS) is a primary bone malignancy, characterized by spindle cells producing osteoid.... more Osteosarcoma (OS) is a primary bone malignancy, characterized by spindle cells producing osteoid. The objective of this study is to describe the magnetic resonance imaging (MRI) features of different OS subtypes, record their attenuation diffusion coefficient (ADC) values and to point to the relation of their pathologic base and their corresponding ADC value. We performed a retrospective observational lesion-based analysis for 31 pathologically proven osteosarcoma subtypes: osteoblastic (n = 9), fibroblastic (n = 8), chondroblastic (n = 6), para-osteal (n = 3), periosteal (n = 1), telangiectatic (n = 2), small cell (n = 1) and extra-skeletal (n = 1). On conventional images we recorded: bone of origin, epicenter, intra-articular extension, and invasion of articulating bones, skip lesions, distant metastases, pathological fractures, ossified matrix, hemorrhage and necrosis. We measured the mean ADC value for each lesion. Among the included OS lesions, 51.6% originated at the femur, 29...
Different reasons can hinder the application of multiparametric programming formulations to solve... more Different reasons can hinder the application of multiparametric programming formulations to solve optimization problems under uncertainty, as the high nonlinearity of the optimization model, and/or its complicated structure. This work presents a complementary method that can assist in such situations. The proposed tool uses kriging metamodels to provide global multiparametric metamodels that approximate the optimal solutions as functions of the problem uncertain parameters. The method has been tested with two benchmark problems of different characteristics, and applied to a case study. The results show the high accuracy of the methodology to predict the multiparametric behavior of the optimal solution, high robustness to deal with different problem types using small number of data, and significant reduction in the solution procedure complexity in comparison with classical multiparametric programming approaches.
The present study was designed to estimate the substantial equivalence of genetically modified (G... more The present study was designed to estimate the substantial equivalence of genetically modified (GM) potato Spunta lines (SpG2, SpG3 and Sp6A-3) with the cry1Ic1 gene compared to conventional non-transgenic potato Spunta cultivar (control group) through rat feeding experiments and through the determination of nutritional composition such as: protein, ash, fiber, fat, starch, total carbohydrates, amino acids, fatty acids, micro and macroelements and vitamins B1, B2 and C. Short-term feeding study was conducted for nine days using experimental rats to estimate the digestibility parameters: True Digestibility, (T.D.), Biological Value (B.V.) and Net Protein Utilization (N.P.U.) as well as the behavior of rats under investigation. Rats in each group (SpG2, SpG3 and Sp6A-3 and control groups) grew normally without marked differences in appearance, behavior or in mortality rate. No marked differences were observed in the T.D., B.V. and N.P.U. in all groups. All tested chemical parameters s...
Abstract Chemical process operation optimization aims at obtaining the optimal operating set-poin... more Abstract Chemical process operation optimization aims at obtaining the optimal operating set-points by real-time solution of an optimization problem that embeds a steady-state model of the process. This task is challenged by unavoidable Uncertain Parameters (UPs) variations. MultiParametric Programming (MPP) is an approach for solving this challenge, where the optimal set-points must be updated online, reacting to sudden changes in the UPs. MPP provides algebraic functions describing the optimal solution as a function of the UPs, which allows alleviating large computational cost required for solving the optimization problem each time the UPs values vary. However, MPP applicability requires a well-constructed mathematical model of the process, which is not suited for process operation optimization, where complex, highly nonlinear and/or black-box models are usually used. To tackle this issue, this paper proposes a machine learning-based methodology for multiparametric solution of continuous optimization problems. The methodology relies on the offline development of data-driven models that accurately approximate the multiparametric behavior of the optimal solution over the UPs space. The models are developed using data generated by running the optimization using the original complex process model under different UPs values. The models are, then, used online to, quickly, predict the optimal solutions in response to UPs variation. The methodology is applied to benchmark examples and two case studies of process operation optimization. The results demonstrate the methodology effectiveness in terms of high prediction accuracy (less than 1% of NRMSE, in most cases), robustness to deal with problems of different natures (linear, bilinear, quadratic, nonlinear and/or black boxes) and significant reduction in the complexity of the solution procedure compared to traditional approaches (a minimum of 67% reduction in the optimization time).
Abstract The complexity of optimization problems of optimization problems increases along with th... more Abstract The complexity of optimization problems of optimization problems increases along with the dimensions, non-linearity, and/or the required accuracy of the model constraints and objective functions. Additionally, for mixed-integer multiparametric problems, the discreet and uncertain nature of the variables and parameters to be considered, affect the complexity further more. Recently, machine learning or data-driven techniques have been proposed as alternatives for the solution of complex multiparametric programming problems. However, those methods presents as a main limitation its very high prediction error in variables that show discrete behavior and on the limits of the critical/local regions. This work extends this investigation line via proposing a novel machine learning method for solving these kind of problems based on an iterative process that use Ordinary Kriging as supervised learning tool to classify and model data. Furthermore, Ordinary Kriging can be also used, as an unsupervised tool to cluster data. The proposed methodology is applied to a benchmark case-study and the numerical results exhibits a significant improvements, up to 65% based on the normalized root-mean-square error, compared with reported information that used other modeling techniques.
This paper investigates the extension of a MultiParametric approach based on surrogate models (Me... more This paper investigates the extension of a MultiParametric approach based on surrogate models (Meta-MultiParametric approach, M-MP) in order to handle general Mixed- Integer (MI) optimization problems involving Uncertain Parameters (UPs). The method harnesses metamodeling and clustering techniques in order to approximate black box relations between the optimal values of the continuous variables and the UPs, while Classification Techniques (CT) are employed to identify the optimal values of the integer variables also as a function of the UPs. The results of applying the method to a benchmark case-study show a high prediction accuracy of the optimal solutions, saving computational effort and overpassing the complex mathematical procedures required by the standard MultiParametric Programming methods.
Abstract This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiS... more Abstract This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiStep-Ahead Prediction (MSAP) of nonlinear systems based on the Ordinary Kriging (OK) metamodel. The OK is used to build a set of Nonlinear Autoregressive models with Exogenous inputs (NAREX), able to approximate the system future outputs as a function of previous inputs and outputs considering a specific delay. Then, these OK-based dynamic models are used in a recursive interactive interpolations scheme to predict the process outputs over several time steps. The capabilities of the OK-based dynamic models are compared to other leading techniques, via their application to benchmark cases. The application results reveal the OK promising and competitive capabilities for MVDM of nonlinear systems, in terms of prediction accuracy and prediction time horizon.
Abstract The digitalization of nuclear power plants, with the rapid growth of information technol... more Abstract The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.
Journal of the Egyptian National Cancer Institute, 2018
Osteosarcoma (OS) is a primary bone malignancy, characterized by spindle cells producing osteoid.... more Osteosarcoma (OS) is a primary bone malignancy, characterized by spindle cells producing osteoid. The objective of this study is to describe the magnetic resonance imaging (MRI) features of different OS subtypes, record their attenuation diffusion coefficient (ADC) values and to point to the relation of their pathologic base and their corresponding ADC value. We performed a retrospective observational lesion-based analysis for 31 pathologically proven osteosarcoma subtypes: osteoblastic (n = 9), fibroblastic (n = 8), chondroblastic (n = 6), para-osteal (n = 3), periosteal (n = 1), telangiectatic (n = 2), small cell (n = 1) and extra-skeletal (n = 1). On conventional images we recorded: bone of origin, epicenter, intra-articular extension, and invasion of articulating bones, skip lesions, distant metastases, pathological fractures, ossified matrix, hemorrhage and necrosis. We measured the mean ADC value for each lesion. Among the included OS lesions, 51.6% originated at the femur, 29...
Different reasons can hinder the application of multiparametric programming formulations to solve... more Different reasons can hinder the application of multiparametric programming formulations to solve optimization problems under uncertainty, as the high nonlinearity of the optimization model, and/or its complicated structure. This work presents a complementary method that can assist in such situations. The proposed tool uses kriging metamodels to provide global multiparametric metamodels that approximate the optimal solutions as functions of the problem uncertain parameters. The method has been tested with two benchmark problems of different characteristics, and applied to a case study. The results show the high accuracy of the methodology to predict the multiparametric behavior of the optimal solution, high robustness to deal with different problem types using small number of data, and significant reduction in the solution procedure complexity in comparison with classical multiparametric programming approaches.
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Papers by Ahmed Shokry