Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model

Engineering with Computers - Over the past few decades, it has been observed a remarkable progression in the development of computer aid models in the field of civil engineering. Machine learning...     

Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier

Pattern Analysis and Applications - This paper presents a reliable and real-time method to detect pedestrians in image scenes that can vary greatly in appearance. To achieve greater reliability in...     

Adsorption of Uranium from Aqueous Solutions by Expanded Perlite

Radiochemistry - The uranium removal from radioactive liquid waste using expanded perlite (EP) was studied. The influence of the contact time, solution pH, initial uranium concentration, sorption...     

Improving through-thickness conductivity of carbon fiber reinforced polymer using carbon nanotube/polyethylenimine at the interlaminar region

The through-thickness conductivity of carbon fiber reinforced polymer (CFRP) composite was increased by incorporating multiwalled carbon nanotubes in the interlaminar region. Carbon nanotubes (CNTs) were dispersed in a polyethylenimine (PEI) binder, which was then coated onto the carbon fiber fabric. Standard vacuum-assisted resin infusion process was applied to fabricate the composite laminates. This modification technique aims to enhance the electrical conductivity in through-thickness direction for the purpose of nondestructive testing, damage detection, and electromagnetic interference shielding. CNT concentrations ranging from 0 to 0.75 wt% were used and compared to pristine CFRP samples (reference). The through-thickness conductivity of the CFRP exhibited an improvement of up to 781% by adopting this technique. However, the dispersion of CNT in PEI led to a viscosity increase and poor wetting properties which resulted in the formation of voids/defects, poor adhesion (as shown in scanning electron micrographs) and the deterioration of the mechanical properties as manifested by interlaminar shear strength and dynamic mechanical analysis measurements.     

New approach to mimic rheological actual shear rate under wall slip condition

Engineering with Computers - The presence of wall slip in concentrated suspensions affect the rheological measurements such as shear stress, shear rate, and viscosity. The measured shear rate will...     

Heat transfer inside a horizontal channel with an open trapezoidal enclosure subjected to a heat source of different lengths

The heat transfer phenomena inside a horizontal channel with an open trapezoidal enclosure subjected to a heat source of different lengths was investigated numerically in the present work. The heat source is considered as a local heating element of varying length, which is embedded at the bottom wall of the enclosure and maintained at a constant temperature. The air flow enters the channel horizontally at a constant cold temperature and a fixed velocity. The other walls of the enclosure and the channel are kept thermally insulated. The flow is assumed laminar, incompressible, and two‐dimensional, whereas the fluid is considered Newtonian. The results are presented in the form of the contours of velocity, isotherms, and Nusselt numbers profiles for various values of the dimensionless heat source lengths (0.16 ≤ ε ≤ 1). while, both Prandtl and Reynolds numbers are kept constant at (Pr = 0.71) and (Re = 100), respectively. The results indicated that the distribution of the isotherms depends significantly on the length of the heat source. Also, it was noted that both the local and the average Nusselt numbers increase as the local heat source length increases. Moreover, the maximum temperature is located near the heat source location.     

Rational Design of Potent Inhibitors of a Metallohydrolase Using a Fragment-Based Approach

Metallohydrolases form a large group of enzymes that have fundamental importance in a broad range of biological functions. Among them, the purple acid phosphatases (PAPs) have gained attention due to their crucial role in the acquisition and use of phosphate by plants and also as a promising target for novel treatments of bone-related disorders and cancer. To date, no crystal structure of a mammalian PAP with drug-like molecules bound near the active site is available. Herein, we used a fragment-based design approach using structures of a mammalian PAP in complex with the Maybridge^TM fragment CC063346, the amino acid L-glutamine and the buffer molecule HEPES, as well as various solvent molecules to guide the design of highly potent and efficient mammalian PAP inhibitors. These inhibitors have improved aqueous solubility when compared to the clinically most promising PAP inhibitors available to date. Furthermore, drug-like fragments bound in newly discovered binding sites mapped out additional scaffolds for further inhibitor discovery, as well as scaffolds for the design of inhibitors with novel modes of action.     

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.