Forward-swept wings were researched and introduced to improve maneuverability, control, and fuel efficiency while reducing drag and they are often used alongside canards, to further enhance their characteristics. In this research, the effects of canard dihedral angles on the wing loading of a forward-swept wing in transonic flow conditions were studied, as the wing loading provides a measure of wing’s efficiency (lift/drag). A generic aircraft model from literatures was selected, simulated, and compared to, using CFD software ANSYS/Fluent where the flow equations were solved to calculate the aerodynamic characteristics. The research was carried at two different Mach numbers, 0.6 and 0.9, for five different canard dihedral angles which tra

This study investigates the performance of granular dead anaerobic sludge (GDAS) bio-sorbent as permeable reactive barrier in removing phenol from a simulated contaminated shallow groundwater. Batch tests have been performed to characterize the equilibrium sorption properties of the GDAS and sandy soil in phenol-containing aqueous solutions. The results of GDAS tests proved that the best values of operating parameters, which achieve the maximum removal efficiency of phenol (=85%), at equilibrium contact time (=3 hr), initial pH of the solution (=5), initial phenol concentration (=50 mg/l), GDAS dosage (=0.5 g/100 ml), and agitation speed (=250 rpm). Fourier transform infrared (FTIR) analysis proved that the carboxylic acid, aromatic, alk

In Computer-based applications, there is a need for simple, low-cost devices for user authentication. Biometric authentication methods namely keystroke dynamics are being increasingly used to strengthen the commonly knowledge based method (example a password) effectively and cheaply for many types of applications. Due to the semi-independent nature of the typing behavior it is difficult to masquerade, making it useful as a biometric. In this paper, C4.5 approach is used to classify user as authenticated user or impostor by combining unigraph features (namely Dwell time (DT) and flight time (FT)) and digraph features (namely Up-Up Time (UUT) and Down-Down Time (DDT)). The results show that DT enhances the performance of digraph features by i

The Dirichlet process is an important fundamental object in nonparametric Bayesian modelling, applied to a wide range of problems in machine learning, statistics, and bioinformatics, among other fields. This flexible stochastic process models rich data structures with unknown or evolving number of clusters. It is a valuable tool for encoding the true complexity of real-world data in computer models. Our results show that the Dirichlet process improves, both in distribution density and in signal-to-noise ratio, with larger sample size; achieves slow decay rate to its base distribution; has improved convergence and stability; and thrives with a Gaussian base distribution, which is much better than the Gamma distribution. The performance depen

The aerodynamic characteristics of general three-dimensional rectangular wings are considered using non-linear interaction between two-dimensional viscous-inviscid panel method and vortex ring method. The potential flow of a two-dimensional airfoil by the pioneering Hess & Smith method was used with viscous laminar, transition and turbulent boundary layer to solve flow about complex configuration of airfoils including stalling effect. Viterna method was used to extend the aerodynamic characteristics of the specified airfoil to high angles of attacks. A modified vortex ring method was used to find the circulation values along span wise direction of the wing and then interacted with sectional circulation obtained by Kutta-Joukowsky theorem of

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D