Cardiac simulation on multi-GPU platform

The cardiac bidomain model is a popular approach to study electrical behavior of tissues and simulate interactions between the cells by solving partial differential equations. The iterative and data parallel model is an ideal match for the parallel architecture of Graphic Processing Units (GPUs). In this study, we evaluate the effectiveness of architecture-specific optimizations and fine grained parallelization strategies, completely port the model to GPU, and evaluate the performance of single-GPU and multi-GPU implementations. Simulating one action potential duration (350 msec real time) for a 256×256×256 tissue takes 453 hours on a high-end general purpose processor, while it takes 664 seconds on a four-GPU based system including the communication and data transfer overhead. This drastic improvement (a factor of 2460×) will allow clinicians to extend the time-scale of simulations from milliseconds to seconds and minutes; and evaluate hypotheses in a shorter amount of time that was not feasible previously.     

Micromachined SU-8-Based Terahertz 8×8 Slotted Waveguide Antenna Array

Journal of Infrared, Millimeter, and Terahertz Waves - This article presents the design and fabrication of a micromachined 300 GHz planar array slotted waveguide antenna utilising the silver-coated...     

Phishing Websites Detection by Using Optimized Stacking Ensemble Model

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent.     

Optimal policies for a finite-horizon batching inventory model

This paper is concerned with finding an optimal inventory policy for the integrated replenishment-production batching model of Omar and Smith (2002). Here, a company produces a single finished product which requires a single raw material and the objective is to minimise the total inventory costs over a finite planning horizon. Earlier work in the literature considered models with linear demand rate function of the finished product. This work proposes a general methodology for finding an optimal inventory policy for general demand rate functions. The proposed methodology is adapted from the recent work of Benkherouf and Gilding (2009).     

Synthesis of novel bis- and tris-(cyclic carbonate)s and their use in preparation of polymer networks

Novel six-membered bis- and tris-(cyclic carbonate)s that are useful as cross-linking agents in the synthesis of structurally stable yet biodegradable polycarbonates and polyesters have been synthesized in good to moderate yield (85-45%). Cross-linked aliphatic polycarbonates, and polyesters were prepared by copolymerization of the bis-cyclic carbonate (3f) with trimethylene carbonate, and ε-caprolactone. The polymers swelled in a wide variety of organic solvents, including CHCl₃, CH₂Cl₂, ethyl acetate, acetone, DMSO, and DMF but not in protic polar solvents, such as methanol, ethanol, and water. Swelling ratios of trimethylene carbonate/3f and ε-caprolactone/3f networks at different feed ratios were investigated in dichloromethane. The glass transition temperature of the TMC/3f networks increased with increasing cross-link density.     

Transition between stratified and non-stratified horizontal oil-water flows. Part II: Mechanism of drop formation

The conditions and mechanism of drop formation at the interface of oil-water wavy stratified flows that lead to the onset of drop entrainment and the transition to dual continuous flow pattern were investigated both experimentally and theoretically. Experimentally, high-speed video imaging was used to capture the mechanism of drop detachment from waves during oil and water stratified flow in a diameter horizontal acrylic pipe. The visual observations revealed that the faster phase undercuts the other one while the waves present in both phases deform until drops are detached. The wave deformation was attributed to the drag force, that originates from the relative movement between the two phases, exceeding the stabilising surface tension force. Based on this force balance an equation was developed that related the wavelength to the amplitude that can lead to drop detachment. This drop entrainment equation and the wave stability equation suggested in part I of the paper [Al-Wahaibi, T., Angeli, P., 2007. Transition between stratified and non-stratified horizontal oil-water flows. Part I: Stability analysis. Chemical Engineering Science, in press, doi:10.1016/j.ces.2007.01.024 ], defined three regions in a wave amplitude versus length graph, namely the stable waves, the unstable waves and the drop entrainment region. The intersection of the lines produced by these two equations gives the critical minimum wave characteristics for drop formation. These agreed well with experimental data when a new correlation for the drag coefficient on the waves was used, suitable for liquid-liquid flows. Also the characteristics of waves that were experimentally found to form drops fell within the predicted entrainment region.     

CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network

This study investigates the use of artificial neural network (ANN) modelling to predict brake power, torque, break specific fuel consumption (BSFC), and exhaust emissions of a diesel engine modified to operate with a combination of both compressed natural gas CNG and diesel fuels. A single cylinder, four-stroke diesel engine was modified for the present work and was operated at different engine loads and speeds. The experimental results reveal that the mixtures of CNG and diesel fuel provided better engine performance and improved the emission characteristics compared with the pure diesel fuel. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception network was used for non-linear mapping between the input and output parameters. It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.9884, 0.9838, 0.95707, and 0.9934 for the engine torque, BSFC, NO_x and exhaust temperature, respectively.