Coupled Ostrovsky equations for internal waves in a shear flow

In the context of fluid flows, the coupled Ostrovsky equations arise when two distinct linear long wave modes have nearly coincident phase speeds in the presence of background rotation. In this paper, nonlinear waves in a stratified fluid in the presence of shear flow are investigated both analytically, using techniques from asymptotic perturbation theory, and through numerical simulations. The dispersion relation of the system, based on a three-layer model of a stratified shear flow, reveals various dynamical behaviours, including the existence of unsteady and steady envelope wave packets.Comment: 47 pages, 39 figures, accepted to Physics of Fluid

Sequential algorithm and numerical analysis on mathematical model for thermal control curing process of thermoset composite materials

To reproduce and improve the efficiency of waste composite materials with consistence and high quality, it is important to tailor and control their temperature profile during curing process. Due to this phenomenon, temperature profile during curing process between two layers of composite materials, which are, resin and carbon fibre are visualized in this paper. Thus, mathematical model of 2D convection-diffusion of the heat equation of thick thermoset composite during its curing process is employed for this study. Sequential algorithms for some numerical approximation such as Jacobi and Gauss Seidel are investigated. Finite difference method schemes such as forward, backward and central methods are used to discretize the mathematical modelling in visualizing the temperature behavior of composite materials. While, the physical and thermal properties of materials used from previous studies are fully employed. The comparisons of numerical analysis between Jacobi and Gauss Seidel methods are investigated in terms of time execution, iteration numbers, maximum error, computational and complexity, as well as root means square error (RMSE). The Fourth-order Runge-Kutta scheme is applied to obtain the degree of cure for curing process of composite materials. From the numerical analysis, Gauss Seidel method gives much better output compared to Jacobi method

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Perilaku Dan Musuh Alami Kupu Endemik Sulawesi Papilio Blumei: Acuan Dalam Konservasi

Papilio blumei is an endemic butterfly of Sulawesi and especially in Bantimurung Bulusaraung National Park. This research was to observed of the behaviour and natural enemies of P. blumei in Bantimurung Bulusaraung National Park. The behaviour of the insect were mating, foraging, competiting, ovipositing and mud-puddling. Life table was used for analysis of mortality factors, therefore the number of mortality was analyzed by key-factors formulation. The result indicated that mating strategies is patrolling. Foraging activity of the sixth instar was the highest compared to the other instars and the lowest one activity of the prapupa stadium of P. blumei. Nectar host plants for the imago of butterfly were Sarcosephalum latifolius and Eugenia sp. There was Scudderia sp. as an interspesific competitor for larval P. blumei. The intraspesific competitor of the imago stage was male of P. blumei. Female P. blumei laid eggs on abaxial leaf E.hupehensis and the eggs hatched after six days. The larva of P. blumei has a overheating behaviour and the adults has a mud puddling. The natural enemies of P.blumei is Trichogramma sp., with k value = 0.381, Pteromalus sp., with k value = 0.125 and Formica sp., with k value = 0.096

SYNTHESIS AND CHARACTERIZATION OF THE HEAVY METALS; AU (III) ,Pd (II),Pt(IV) Rh(III) COMPLEXES OF S –PROPYNYL 2 –THIOBENZIMIDAZOLE

Complexes of Au(III) ,Pd (II) , Pt (IV ) and Rh(III) with S – propynyle -2- thiobenzimidazole (BENZA) have been prepared and characterized by IR and UV- Visible spectral methods in addition to magnetic and conductivity measurements and micro – elemental analysis (CHN).The probable structures of the new complexes have been suggested

On Contra SS-Continuous Functions

In this paper, we apply the notion of -open set in topological spaces to introduce and investigate the concept of contra -continuous which is a subclass of the class of contra semi continuous functions. Keywords: -closed, contra -continuous, contra SS –closed and strongly contra SS –closed

Complexes of Some Transition Metal with 2-Benzoyl thiobenzimidazole and 1,10-Phenanthroline and Studying their Antibacterial Activity

Mixed ligands of 2-benzoyl Thiobenzimiazole (L1) with 1,10-phenanthroline (L2) complexes of Cr(III) , Ni(II) and Cu(II) ions were prepared. The ligand and the complexes were isolated and characterized in solid state by using FT-IR, UV-Vis spectroscopy, 1H, 13C-NMR, flame atomic absorption, elemental micro analysis C.H.N.S, magnetic susceptibility , melting points and conductivity measurements. 2-Benzoyl thiobenzimiazole behaves as bidenetate through oxygen atom of carbonyl group and nitrogen atom of imine group. From the analyses Octahedral geometry was suggested for all prepared complexes. A theoretical treatment of ligands and their metal complexes in gas phase were studied using HyperChem-8 program, moreover, ligands in gas phase also has been studied using Gaussian program (GaussView Currently Available Version (5.0.9) along with Gaussian 09 which was the latest in the Gaussian series of programs). The antibacterial activity of the prepared complexes have been determined and compared with that of the ligand and the standard metronidazole

Effect of Heat Treatment on Corrosion Behaviour of Welded AA6061 Aluminium Alloy in Seawater

The effect of various heat treatments on the corrosion behaviour of welded AA6061 aluminium alloy was investigated. Gas tungsten arc welding (GTAW) was used for welding butt joint specimens. Corrosion behaviour was determined in seawater solution using potentiodynamic polarization method. Microstructure and compositional analysis of base metal (BM) and weld metal (WM) was studied with scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS). The result indicated that BM consists of Fe-rich coarse intermetallic particles that behave as cathodic sites with respect to the matrix. Tafel plot showed that WM is cathode and give better corrosion resistance in different heat treatment compared to BM. Localized corrosion was observed on the corroded surfaces by SEM