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    A rigid thorax assumption affects model loading predictions at the upper but not lower lumbar levels

    , Article Journal of Biomechanics ; Volume 49, Issue 13 , 2016 , Pages 3074-3078 ; 00219290 (ISSN) Ignasiak, D ; Ferguson, S. J ; Arjmand, N ; Sharif University of Technology
    Elsevier Ltd 
    Abstract
    A number of musculoskeletal models of the human spine have been used for predictions of lumbar and muscle forces. However, the predictive power of these models might be limited by a commonly made assumption; thoracic region is represented as a single lumped rigid body. This study hence aims to investigate the impact of such assumption on the predictions of spinal and muscle forces. A validated thoracolumbar spine model was used with a flexible thorax (T1–T12), a completely rigid one or rigid with thoracic posture updated at each analysis step. The simulations of isometric forward flexion up to 80°, with and without a 20 kg hand load, were performed, based on the previously measured... 

    Thermal wall model effect on the lid-driven nanocavity flow simulation using the molecular dynamics method

    , Article Numerical Heat Transfer, Part B: Fundamentals ; Volume 63, Issue 3 , Jan , 2013 , Pages 248-261 ; 10407790 (ISSN) Darbandi, M ; Sabouri, M ; Jafari, S ; Sharif University of Technology
    2013
    Abstract
    An accurate molecular dynamics simulation of the nanocavity flow cannot be achieved without considering correct thermal treatments for the molecules both distributed in the flow and located at the cavity walls and without including their interactions correctly. In this study, we specify constant temperature at the nanocavity vertical walls; however, we examine three different thermal wall models, including a rigid wall, a controlled-temperature flexible wall, and a noncontrolled-temperature flexible wall, to model the horizontal wall behaviors. Comparing the results of these three models with each other, it is possible to evaluate the effect of wall model on the resulting temperature and... 

    Performance evaluation of energy management system in smart home using wireless sensor network

    , Article 2012 2nd Iranian Conference on Smart Grids, ICSG 2012, 23 May 2012 through 24 May 2012 ; May , 2012 , Page(s): 1 - 8 ; 9781467313995 (ISBN) Maghsoodi, N. H ; Haghnegahdar, M ; Jahangir, A. H ; Sanaei, E ; Sharif University of Technology
    IEEE  2012
    Abstract
    In this paper we evaluate the performance of Energy Management and Building Automation Systems in smart environment using wireless sensor network. We describe some aspects of smart grid and then explain the related communication models of smart meters and building management or energy management systems in a smart home. We propose three models, then we compare these models and we select a model for our test bed. We implement the selected model in real environment using wireless sensor network. We analyse network delay to find out whether wireless sensor network is useful or not. With proposed model, we can save up to 57% in energy cost. Also, we develop In- home display to inform customers... 

    Evolving application of machine learning in the synthesis of CHA/ZrO2 nanocomposite for the microhardness prediction

    , Article Materials Letters ; Volume 327 , 2022 ; 0167577X (ISSN) Hasani, A ; Shojaei, M. R ; Khayati, G. R ; Sharif University of Technology
    Elsevier B.V  2022
    Abstract
    Nanocomposites containing ZrO2 and HA have been considered in various fields due to their unique mechanical properties. The principal purpose of this paper is to select the models with the maximum accuracy for the prediction of microhardness of CHA/ZrO2 nanocomposite. For this purpose, three models, including gene expression programming (GEP), gray wolf optimization algorithm (GWOA), and least squares support vector machine (LS-SVM), were implemented to predict and optimize the microhardness of the CHA/ZrO2 nanocomposite. Finally, the results showed that the data obtained from the LS-SVM model were closer to the preliminary data than the others. According to the results, the LS-SVM could... 

    Prediction of particle deposition in the respiratory track using 3D-1D modeling

    , Article Scientia Iranica ; Volume 19, Issue 6 , December , 2012 , Pages 1479-1486 ; 10263098 (ISSN) Monjezi, M ; Dastanpour, R ; Saidi, M. S ; Pishevar, A. R ; Sharif University of Technology
    2012
    Abstract
    Airflow simulation of the whole respiratory system is still unfeasible due to the geometrical complexity of the lung airways and the diversity of the length scales involved in the problem. Even the new CT imaging system is not capable of providing accurate 3D geometries for smaller tubes, and a complete 3D simulation is impeded by the limited computational resources available. The aim of this study is to develop a fully coupled 3D-1D model to make accurate prediction of airflow and particle deposition in the whole respiratory track, with reasonable computational cost and efficiency. In the new proposed method, the respiratory tree is divided into three parts to be dealt with using different... 

    Comparison of mouse embryo deformation modeling under needle injection using analytical Jacobian, nonlinear least square and artificial neural network techniques

    , Article Scientia Iranica ; Volume 18, Issue 6 , 2011 , Pages 1486-1491 ; 10263098 (ISSN) Abbasi, A. A ; Ahmadian, M. T ; Vossoughi, G. R ; Sharif University of Technology
    Abstract
    Analytical Jacobian, nonlinear least square and three layer artificial neural network models are employed to predict deformation of mouse embryos under needle injection, based on experimental data captured from literature. The Maximum Absolute Error (MAE), coefficient of determination ( R2), Relative Error of Prediction (REP), Root Mean Square Error of Prediction (RMSEP), NashSutcliffe coefficient of efficiency ( Ef) and accuracy factor ( Af) are used as the basis for comparison of these three models. Analytical Jacobian, nonlinear least square and ANN models have yielded the correlation coefficient of 0.9985, 0.9964 and 0.9998, respectively. The REP between the models predicted values and...