Browsing by Author "Olumegbon, Ismail A."

Now showing 1 - 8 of 8
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    Characterizing human subchondral bone properties using near-infrared (NIR) spectroscopy
    (Nature : Scientific Report, 2018-06-27) Afara, Isaac O.; Florea, Cristina; Olumegbon, Ismail A.; Eneh, Chibuzor T.; Malo, Markus K. H.; Korhonen, Rami K.; Töyräs, Juha
    Degenerative joint conditions are often characterized by changes in articular cartilage and subchondral bone properties. These changes are often associated with subchondral plate thickness and trabecular bone morphology. Thus, evaluating subchondral bone integrity could provide essential insights for diagnosis of joint pathologies. This study investigates the potential of optical spectroscopy for characterizing human subchondral bone properties. Osteochondral samples (n = 50) were extracted from human cadaver knees (n = 13) at four anatomical locations and subjected to NIR spectroscopy. The samples were then imaged using micro-computed tomography to determine subchondral bone morphometric properties, including: plate thickness (Sb.Th), trabecular thickness (Tb.Th), volume fraction (BV/TV), and structure model index (SMI). The relationship between the subchondral bone properties and spectral data in the 1st (650–950 nm), 2nd (1100–1350 nm) and 3rd (1600–1870 nm) optical windows were investigated using partial least squares (PLS) regression multivariate technique. Significant correlations (p < 0.0001) and relatively low prediction errors were obtained between spectral data in the 1st optical window and Sb.Th (R2 = 92.3%, error = 7.1%), Tb.Th (R2 = 88.4%, error = 6.7%), BV/TV (R2 = 83%, error = 9.8%) and SMI (R2 = 79.7%, error = 10.8%). Thus, NIR spectroscopy in the 1st tissue optical window is capable of characterizing and estimating subchondral bone properties, and can potentially be adapted during arthroscopy
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    Characterizing human subchondral bone properties using near-infrared (NIR) spectroscopy
    (Nature Publishing Group, 2018-06-27) Afara, Isaac O.; Florea, Cristina; Olumegbon, Ismail A.; Eneh, Chibuzor T.; Malo, Markus K. H.; Korhonen, Rami K.; Töyräs, Juha
    Degenerative joint conditions are often characterized by changes in articular cartilage and subchondral bone properties. These changes are often associated with subchondral plate thickness and trabecular bone morphology. Thus, evaluating subchondral bone integrity could provide essential insights for diagnosis of joint pathologies. This study investigates the potential of optical spectroscopy for characterizing human subchondral bone properties. Osteochondral samples (n = 50) were extracted from human cadaver knees (n = 13) at four anatomical locations and subjected to NIR spectroscopy. The samples were then imaged using micro-computed tomography to determine subchondral bone morphometric properties, including: plate thickness (Sb.Th), trabecular thickness (Tb.Th), volume fraction (BV/TV), and structure model index (SMI). The relationship between the subchondral bone properties and spectral data in the 1st (650–950 nm), 2nd (1100–1350 nm) and 3rd (1600–1870 nm) optical windows were investigated using partial least squares (PLS) regression multivariate technique. Significant correlations (p < 0.0001) and relatively low prediction errors were obtained between spectral data in the 1st optical window and Sb.Th (R2 = 92.3%, error = 7.1%), Tb.Th (R2 = 88.4%, error = 6.7%), BV/TV (R2 = 83%, error = 9.8%) and SMI (R2 = 79.7%, error = 10.8%). Thus, NIR spectroscopy in the 1st tissue optical window is capable of characterizing and estimating subchondral bone properties, and can potentially be adapted during arthroscopy.
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    Lattice constant prediction of A2XY6 cubic crystals (A = K, Cs, Rb, TI; X = tetravalent cation; Y = F, Cl, Br, I) using computational intelligence approach
    (American Institute of Physics, 2020-01-02) Alade, Olanrewaju I.; Olumegbon, Ismail A.; Bagudu, Aliyu
    Lattice constant mismatch between materials affects the quality of thin film fabrication. For this reason, lattice constants information is vital in the design of materials for technological applications. The determination of lattice constants via experimental analysis is relatively expensive and laborious. As a result, several linear empirical models have been proposed to predict the lattice constant of crystal structures. However, the accuracies of these models are limited partly due to their failure to account for nonlinearity in the atomic parameters-lattice constant relationship. Machine learning techniques have shown excellent ability to deal with nonlinear problems in many areas of materials science; hence, they are considered suitable computation tools to study the crystal structure of materials. In this contribution, we developed a support vector regression (SVR) model to predict the lattice constant of cubic crystals of the form A2XY6 (A = K, Cs, Rb, TI; X = tetravalent cation; and Y = F, Cl, Br, I). The SVR algorithm uses the ionic radii and electronegativities data of the constituent elements of A2XY6 cubic crystals as model inputs. The robustness of the proposed model was demonstrated by comparing our result with an existing linear model based on 26 cubic crystal samples. The result revealed a total relative deviation of 1.757 and 2.704 for the SVR model and the existing linear equation, respectively. This result proves that the SVR model has a huge potential in the search for new materials for different applications.
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    Modelling the viscosity of carbon-based nanomaterials dispersed in diesel oil: a machine learning approach
    (Journal of Thermal Analysis and Calorimetry, 2021) Olumegbon, Ismail A.; Alade, Ibrahim O.; Sahaluddin, Mirza; Oyedeji, Mojeed Opeyemi; Sa’ad, Aliyu Umar
    The viscosity of a nanofluid is one of its fundamental thermophysical properties, and it is an important consideration in heat transfer applications. Although the viscosity can be reliably obtained from experimental measurements, the development of models to predict the viscosity is a faster and more convenient approach. This study focuses on creating a machine learning model for the viscosity of different carbon nanomaterials dispersed in diesel oil. The nanomaterials considered here include multi-walled carbon nanotubes, graphene nanoplatelets, and their hybrid combinations. A support vector regression-based model was developed and validated using 120 experimental data points in the temperature range 5–100 °C. The model inputs are the nanoparticle mass fraction, the fluid temperature, and the viscosity of the diesel oil. The developed model yields very good predictive performance on the training and testing datasets. The correlation coefficient and the root mean square error were 99.98% and 0.0076 Pa s, respectively, for the training dataset, and 99.99% and 0.0026 Pa s for the testing dataset. These results indicate that the developed model is extremely accurate for predicting the viscosity of carbon-based nanomaterials in a diesel oil medium, and it was found to outclass all existing models. This model could therefore be extremely useful in the design of heat transfer applications.
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    Near-infrared (NIR) spectroscopic evaluation of articular cartilage: A review of current and future trends
    (Taylor & Francis, 2017-07-03) Olumegbon, Ismail A.; Oloyede, Adekunle; Afara, Isaac O.
    This review describes recent developments and applications of near-infrared (NIR) spectroscopy for characterization of articular cartilage integrity. It summarizes the research findings in this area and presents some spectral ranges and peaks associated with the different properties and components of articular cartilage. We further describe recent adaptations of NIR spectroscopy for clinical evaluation of articular cartilage injury and degeneration. Critical to accurate decision-making during repair surgery is having clear knowledge of lesion severity and spread, and how to grade the quality of surrounding cartilage. Thus, in this review, we detail efforts aimed at quantification and classification of cartilage pathology using NIR spectroscopy. Finally, we present open questions and challenges with a view to guiding future directions in NIR spectroscopy research on articular cartilage.
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    On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach
    (Journal of Energy Storage, 2020-10-29) Jamei, Mehdi; Ahmadianfar, Iman; Olumegbon, Ismail A.; Karbasi, Masoud; Asadi, Amin
    To characterize the performance of nanofluids for heat transfer applications in solar systems, an accurate estimation of their specific heat capacity (SHC) is of paramount importance. To this end, having such properties of nanofluids via computational approaches has gained attention as an effective method to eliminate the timeconsuming process of experimental investigations. This study focuses on modeling the SHC of different carbon-based and metal oxide-based nanoparticles dispersed in various base fluids. Herein, we propose a novel data-driven dynamic model based on the Gaussian process regression (GPR) technique in comparison with the random forest (RF) approach and generalized regression neural network (GRNN) to predict the SHC of nanofluids. The developed models employ the solid volume fraction (ϕ), temperature (T), mean diameter of nanoparticle (Dp), and SHC of base fluid (CPBase) as the input parameters. The data has been collected from 10 reliable references. The results showed that the GPR model (R=0.99974, RMSE=0.01506 J/K.g) shows superior performance than the results of the RF (R=0.99761, RMSE=0.04598 J/K.g) and GRNN (R=0.99563, RMSE=0.06085 J/K.g). The results proved that the developed model would accurately estimate the SHC of the studied nanofluids. In addition, the sensitivity analysis of the dependence of input variables on the SHC of nanofluids revealed that the mean diameter of nanoparticles and the SHC of base fluid are the major critical factors in the determination of SHC of nanofluids.
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    On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective – A comprehensive assessment of data analysis techniques
    (International Communications in Heat and Mass Transfer, 2021) Jamei, Mehdi; Ahmadianfara, Iman; Olumegbon, Ismail A.; Asadi, Amin; Karbasi, Masoud; Said, Zafar; Sharifpur, Mohsen; Meyer, Josua P.
    The main aim of the present study is to investigate the capabilities of four robust machine learning method - the Kernel Extreme Learning Machine (KELM), Adaptive Regression Spline (MARS), M5 Model Tree (M5Tree), and Gene Expression Programming (GEP) model in predicting specific heat capacity (SHC) of metal oxide-based nanofluids implemented in solar energy application. Sets of 1180 data of different metal oxide-based nanofluids containing Al2O3, ZnO, TiO2, SiO2, MgO, and CuO dispersed in various base fluids were collected from reliable literature to provide the predictive model of SHC of nanofluids. The volume fraction, temperature, SHC of the base fluid, and mean diameter of nanoparticles were used as an input variable to predict nanofluids' SHC as the output variable. The artificial intelligence (AI) models were validated using several statistical performance criteria, graphical devices, and conventional models. The results obtained from all datasets demonstrated that the KELM model significantly outperformed the MARS, M5Tree, and GEP model in predicting the SHC of nanofluid. Moreover, the sensitivity analysis showed that the mean diameter of the nanoparticle and SHC of the base fluid have the most considerable impact on estimating the SHC of metal oxide-based nanofluids.
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    On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network
    (International Journal of Heat and Mass Transfer, 2021) Jamei, Mehdi; Olumegbon, Ismail A.; Karbasi, Masoud; Ahmadianfar, Iman; Asadi, Amin; Mosharaf-Dehkordif, Mehdi
    Regarding their ability to enhance conventional thermal oils' thermophysical properties, oil-based hybrid nanofluids have recently been widely investigated by researchers, especially on lubrication and cooling application in the automotive industry. Thermal conductivity is one of the most crucial thermophysical properties of oil-based hybrid nanofluids, which has been studied in a minimal case of studies on the specific types of them. In this research, for the first time, a comprehensive data-intelligence analysis performed on 400 gathered data points of various types of oil-based hybrid nanofluids using a novel hybrid machine learning approach; the Extended Kalman Filter-Neural network (EKF-ANN). The genetic programming (GP) and response surface methodology (RSM) approaches were examined to appraise the main paradigm. In this research, the best subset regression analysis, as a novel feature selection scheme, was provided for finding the best input parameter among all existing predictive variables (the volume fraction, temperature, thermal conductivity of the base fluid, mean diameter, and bulk density of nanoparticles). The provided models were examined using several statistical metrics, graphical tools and trends, and sensitivity analysis. The results assessment indicated that the EKF-ANN in terms of (R=0.9738, RMSE=0.0071 W/m.K, and KGE=0.9630) validation phase outperformed the RSM (R=0.9671, RMSE=0.0079 W/m.K, and KGE=0.9593) and GP (R=0.9465, RMSE=0.010 W/m.K, and KGE=0.9273), for accurate estimation of the thermal conductivity of oil-based hybrid nanofluids.