Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Fuzzy logic. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Electrospun nanofiber-based regeneration of cartilage enhanced by mesenchymal stem cells

Abstract: Application of biomaterials in combination with stem cells is a novel tissue engineering approach to regenerate cartilage. The objective of this study was to investigate the potential of poly(vinyl alcohol)/polycaprolactone (PVA/PCL) nanofiber scaffolds seeded with rabbit bone marrow-mesenchymal stem cell (BM-MSC) for cartilage tissue engineering in vitro and in vivo. We tested the biocompatibility and mechanical properties of nanofibrous scaffolds using scanning electron microscope, MTT assay, and tensile measurements. The capacity of MSC for chondrogenic differentiation on scaffolds was examined using reverse transcription-polymer chain reaction and immunostaining. For in vivo assessments, PVA/PCL nanofiber scaffolds with or without MSC were implanted into rabbit full-thickness cartilage defects. To evaluate cartilage regeneration, semi-quantitative grading and histological analysis were performed. Our results showed that PVA/PCL scaffolds supported the proliferation and chondrogenic differentiation of MSC in vitro. Moreover, the animals treated with cell-seeded PVA/PCL scaffolds showed improved healing of defects compared with untreated control and those which received cell-free scaffolds. Our findings suggest that PVA/PCL scaffolds incorporated with MSC can serve as a suitable graft for articular cartilage reconstruction.

Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil

Abstract: The application of models provided by artificial neural network (ANN) in predicting bearing capacity of driven pile is underlined in several investigations. However, weakness of ANN in slow rate of convergence as well as finding reliable testing output is known to be the major drawbacks of implementing ANN-based techniques. The present study aims to introduce and evaluate an optimized ANN with imperialism competitive algorithm (ICA) model based to estimate bearing capacity of driven pile in cohesionless soil. The training data for optimizing the ICA-ANN structure are based on the in situ study. To develop the ICA-ANN model, the input parameters are internal friction angle of soil located in shaft (φ shaft), and tip (φ tip), pile length (L), effective vertical stress at pile toe (σ v), and pile area (A) while the output is the total driven pile bearing capacity in cohesionless soil. The predicted results are compared with a pre-developed ANN model to demonstrate the ability of the hybrid model. As a result, coefficient of determination (R 2) values of (0.885 and 0.894) and (0.964 and 0.974) was obtained for testing and training datasets of ANN and ICA-ANN models, respectively. In addition, values of variance account for (VAF) of (88.212 for training and 89.215 for testing) and (96.369 for training and 97.369 for testing, respectively) were obtained for ANN and ICA-ANN models, respectively. The obtained results declare high reliability of the developed ICA-ANN model. This model can be introduced as a new model in field of deep foundation engineering.

A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization

Abstract: Fault detection and diagnosis have an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance technique that is applicable in the fault diagnosis of rotating machinery faults. A unique flexible algorithm is proposed for classifying the condition of centrifugal pump based on support vector machine hyper-parameters optimization and artificial neural networks (ANNs) which are composed of eight distinct steps. Artificial neural networks (ANNs), support vector classification with genetic algorithm (SVC-GA) and support vector classification with particle swarm optimization (SVC-PSO) algorithm have been considered in a flexible algorithm to perform accurate classification in the manufacturing area. SVC-GA, SVC-PSO and ANN have been used together due to their importance and capabilities in classifying domain. Also, the superiority of the proposed hybrid algorithm (SVC with GA and PSO) is shown by comparing its results with SVC performance. Two types of faults through six features, flow, temperature, suction pressure, discharge pressure, velocity, and vibration, have been classified with proposed integrated algorithm. To test the robustness of the efficiency results of the proposed method, the ability of proposed flexible algorithm in dealing with noisy and corrupted data is analyzed.