Entropy analysis of unsteady MHD three-dimensional flow of Williamson nanofluid over a convectively heated stretching sheet

This article addresses an investigation of the entropy analysis of Williamson nanofluid flow in the presence of gyrotactic microorganisms by considering variable viscosity and thermal conductivity over a convectively heated bidirectionally stretchable surface. Heat and mass transfer phenomena have been incorporated by taking into account the thermal radiation, heat source or sink, viscous dissipation, Brownian motion, and thermophoretic effects. The representing equations are nonlinear coupled partial differential equations and these equations are shaped into a set of ordinary differential equations via a suitable similarity transformation. The arising set of ordinary differential equations was then worked out by adopting a well-known scheme, namely the shooting method along with the Runge-Kutta-Felberge integration technique. The effects of flow and heat transfer controlling parameters on the solution variables are depicted and analyzed through the graphical presentation. The survey finds that magnifying viscosity parameter, Weissenberg number representing the non-Newtonian Williamson parameter cause to retard the velocity field in both the directions and thermal conductivity parameter causes to reduce fluid temperature. The study also recognizes that enhancing magnetic parameters and thermal conductivity parameters slow down the heat transfer rate. The entropy production of the system is estimated through the Bejan number. It is noticeable that the Bejan number is eminently dependent on the heat generation parameter, thermal radiation parameter, viscosity parameter, thermal conductivity parameter, and Biot number. The skillful accomplishment of the present heat and mass transfer system is achieved through the exteriorized choice of the pertinent parameters.     

Analytical approach on magnetohydrodynamic Casson fluid flow past a stretching sheet via Adomian decomposition method

Flow phenomena of three-dimensional conducting Casson fluid through a stretching sheet are proposed in the present investigation with the impact of the magnetic parameter in a permeable medium. The adaptation of particular transformations is useful to modify the governing equations into their nondimensional as well as the ordinary form. However, these transformed equations are nonlinear and approximate analytical methods for the solution of the complex form of governing equations. In particular, the Adomian decomposition method is proposed for the solution. The behavior of several variables, such as the magnetic and porous matrix, on the flow profile as well as the rate of shear stress, are discussed via graphs and tables. The conformity of the current result with the earlier study shows a road map for further investigation. The major concluding remarks are; the retardation in the velocity distribution is rendered due to an increase in the Casson parameter moreover, the Casson parameter favors in reducing the rate of shear stress coefficient in magnitude.     

Detecting DDoS Attacks in Cloud Computing Using Extreme Learning Machine and Adaptive Differential Evolution

Wireless Personal Communications - Distributed denial of service (DDoS) attacks disrupt the availability of cloud services. The detection of these attacks is a major challenge in the cloud...     

An embedded FPGA-SoC framework and its usage in moving object tracking application

Design Automation for Embedded Systems - Moving object tracking is a computation-intensive operation that requires accelerating hardware solution. In this work, a high-performance design for mean...     

Dictionary-based classifiers for exploiting feature sequence information and their application to hyperspectral remotely sensed data

The problem of classification is shared across various disciplines. Designing even less computationally demanding and more effective classifiers has been a key challenge for researchers for many years. No single classifier can be highly effective for all types of datasets and thus, depending on the data distribution, various classifiers have been proposed in the literature. To our knowledge, feature values have been vastly exploited as the base for discriminating classes, while feature sequence information has been somehow under-exploited so far. In the proposed approach normalised features are sorted and ranked, creating a sequence of finite numbers. The associated rank of the created sequence is used as an additional feature, which in a way defines the sample-specific intra-feature relationship. Three novel dictionary-based approaches such as Sequence Classifier (SC), Sequence-dictionary-based k-Nearest Neighbours Classifier (SDk-NN) and Combined-dictionary-based k-Nearest Neighbours Classifier (CDk-NN) are proposed in this paper.

In the case of remotely sensed data, and specifically in Hyper-Spectral Images (HSI), the spectral features (Spectral signatures) represent a strong, object-specific spectral relationship, which is a key point in our proposed approach. In this case, indeed, the proposed classifiers were tested over various (five) HS datasets and found to be effective. Based on the classifiers features, two derived distance measures are proposed and validated for the HS dataset, namely: the Normalised Sequence Distance (NSD) measure and Combined Distance (CD) measure. These measures appear to overperform the conventional Normalised Euclidean Distance (NED) in this context. Also, validation for both binary and multi-class datasets are experimented and their performances are evaluated in terms of accuracy and other standard measures. Experimental results over 21 datasets revealed that the proposed approaches perform comparably, and in some cases even better than other classifiers. Stack-operated, class-specific sparse dictionaries are also introduced in order to reduce the computational complexity, which can be used as an active learning-based approach for optimal training sample selection. Additional tests were performed with variable levels of dictionary sparsity for assessing its impact on accuracy.     

Introducing the Planar Laser-Induced Fluorescence Technique (PLIF) to Measure Mixing Time in Gas-Stirred Ladles

Metallurgical and Materials Transactions B - The planar laser-induced fluorescence (PLIF) technique was implemented to measure mixing time in a 1/17 water model of a 200-ton ladle furnace. The...     

TiO2 nano-flakes with high activity obtained from phosphorus doped TiO2 nanoparticles by hydrothermal method

TiO₂ nano-wires (Ti-NWs) and nano-flakes (Ti-NFs) were obtained from phosphorus doped TiO₂ nanoparticles (Ti-P) by hydrothermal method and by subsequent heat treatment respectively. FE-SEM micrograph of the as prepared sample depicts well formed, entangled and randomly oriented nano-wires morphology, which changes to nano-flakes morphology after heat treatment. Structural characterization of the samples by X-ray diffraction shows anatase phase for both the samples. Absorption edge of the Ti-NWs sample shows blueshift where as the Ti-NFs sample exhibit redshift compared to precursor sample as evidenced by UV–Visible absorption spectra, which is due to change in morphology and crystallinity of the samples. XPS studies indicate the presence of titanium and oxygen species only. From the EPR measurements with in-situ visible light irradiation, the number of photogenerated charge carriers is found to be very high for nano-flakes sample. Methyleneblue degradation profiles depict very high activity of Ti-NFs sample compared to Ti-NWs and the precursor samples, which is due to the observed redshift in the absorption edge, change in morphology and high crystallinity of the sample which in turn increases the optical response and separation of photogenerated charge carriers as evidenced by the optical and EPR measurements respectively.