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

Zinc Oxide Nanostructures for Efficient Energy Conversion in Organic Solar Cell

We present a new approach of solution-processed using zinc oxide (ZnO) nanostructures as extraction layer material for organic solar cells. It is low chemical reaction compatibility with all types of organic blends and its good adhesion to both surfaces of ITO/glass substrate and the active layer (blends). Parameters such as the thickness and the morphology of the films were investigated to prove that these factors greatly affect the efficiency of organic solar cells. In this work, ZnO layer with thickness of approximately 53 nm was used as an interlayer to prevent pin-holes between the electrode and the polymer layer. The polymer layer was coated on the ZnO layer with the thickness of about 150 nm. The thick polymer layer will form a non-uniform surface because of the solvent, 1-2dichlorobenzene will etch away some region of the polymer layer and forming pin-holes. ZnO nanostructures layer was used to prevent pin-holes between the polymer layer and electrode. From the surface morphology of ZnO layer, it shows a uniform surface with particle grain size obtained between 50 -100 nm. The presence of the interlayer has a positive effect on the electrical characteristics of the solar cells. It was found that an organic solar cell with thickness less than 150 nm shows the optimum performance with efficiency of 0.0067% and Fill Factor (FF) of about 19.73

Dynamic Mechanical and Gel Content Properties of Irradiated ENR/PVC Blends with TiO2 Nanofillers

Numerous studies reported on irradiated epoxidized natural rubber/polyvinyl chloride (ENR/PVC) blends and the blends were found miscible at all compositional range thus it offers a broad of opportunity in modifying the blend characteristic. Addition of low loading titanium dioxide (TiO2) nanofillers in the ENR/PVC blends has shown a remarkable increment in tensile strength. Thus, this study was initiated to address the effect of TiO2 nanofillers on ENR/PVC blends dynamic mechanical and gel content properties and its morphology upon exposure to electron beam irradiation. ENR/PVC blends with addition of 0, 2 and 6 phr TiO2 nanofillers were first blended in a mixing chamber before being irradiated by an electron beam accelerator at different 0-200 kGy irradiation doses. The influence of TiO2 nanofillers on the irradiation crosslinking of ENR/PVC blends was study based on the dynamic mechanical analysis which was carried out in determining the glass transition temperature and the storage modulus behavior of ENR/PVC blends incorporated with TiO2 nanofillers. Formations of irradiation crosslinking in the blend were investigated by gel content measurement. While, the TiO2 nanofillers distribution were examined by Transmission Electron Microscope (TEM). Upon irradiation, the ENR/PVC/6 phr TiO2 formed the highest value of gel fraction. For dynamic mechanical analysis, it was found that electron beam radiation increased the Tg of all the compositions. The relationship between the crosslinking and the stiffness of the nanocomposites also can be found in this study. The enhancement in the storage modulus and Tg at higher amount of TiO2 in the blend could be correlated to the enhancement of the irradiation-induced crosslinking in the nanocomposites characteristic and also with the higher agglomerations of TiO2 evidence shown from the TEM micrograph examination. Lastly, the dimensions of TiO2 in the blends were found less than 100 nm in diameter which indicates incorporation of TiO2 nanofillers in ENR/PVC blends is potentially to provide the nanocomposites features. Doi: 10.12777/ijse.6.1.24-30 [How to cite this article: Ramlee, N.A., Ratnam, C.T., Alias, N.H., Rahman, M.F.A.. 2014. Dynamic Mechanical and Gel Content Properties of Irradiated ENR/PVC blends with TiO2 Nanofillers. International Journal of Science and Engineering, 6(1),24-30. Doi: 10.12777/ijse.6.1.24-30

Parallel computing of numerical schemes and big data analytic for solving real life applications

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance

Business intelligence readiness factors for higher education institution

Higher Education Institution (HEI) have embarked on the new style of decision-making with the aim to enhance the speed and reliability of decision-making capabilities. One of the hardest challenges in implementing Business Intelligence (BI) is the organization’s readiness towards adopting and implementing BI systems. Currently, few published studies have examined BI readiness in HEI environment. Seeing this challenge, this study aims to contribute in determining the BI readiness factors in HEI specifically in the deployment strategies. Through inductive attention to BI in HEI environment, three broad factors have been identified: a) Organizational – that concerning on business strategies, process and structure, b) Technology – involves the BI system and knowledge for managing including the sources and c) Social – the culture within organization that may influence decision-making and its processes. This paper also makes recommendations for future research

Chemically treated chicken bone waste as an efficient adsorbent for removal of acetaminophen

Present of pharmaceutical as the emerging pollutants arise the concerns of environment community regarding the potential impact of acetaminophen (ACT) on ecological and human health. Adsorption process has been proven as an effective treatment being activated carbon as the adsorbent to remove many types of pollutant including low concentration of pollutants. However, on large scale industrial processes, utilisation of activated carbon is limited because of their high production cost. Synthesis of waste materials as a precursor of adsorbent is an attractive approach in sustainable management and economic availability. In this study, the removal of ACT from aqueous solution by chemically treated chicken bone (AC) waste was investigated. The adsorption process was conducted in a batch adsorption and affected by several experimental parameters including contact time, pH, adsorbent dose, initial concentration and temperature. With AC dosage of 0.1 g about 93 % of 1,000 mg/L ACT was removed from the aqueous solution that had pH of 2 and temperature of 25 °C. Kinetic of ACT adsorption was well described by pseudo-second order kinetic model. Meanwhile, effect of initial concentration of acetaminophen adsorption data was fitted well with Freundlich isotherm model with an R2 of 0.9909. Finally, the data obtained from effect of temperature was used to determine the adsorption thermodynamic including the enthalpy, ΔH, Gibbs energy, ΔG and entropy, ΔS. It was found that the ΔG was negative at all temperature while both, ΔH and ΔS was also negative between temperatures of 25 °C to 70 °C indicating the process of ACT adsorption was exothermic reaction and the adsorption reaction is spontaneous at low temperature

Efficient removal of partially hydrolysed polyacrylamide in polymer-flooding produced water using photocatalytic graphitic carbon nitride nanofibres

In this work, graphitic carbon nitride (GCN) photocatalyst-incorporated polyacrylonitrile (PAN) nanofibres (GCN/PAN nanofibres) were successfully prepared using electrospinning technique. The physicochemical properties of the fabricated GCN/PAN nanofibres were analysed using field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), elemental analyser, X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and UV–vis–NIR spectroscopy. The photocatalytic degradation by GCN/PAN nanofibres exhibited 90.2% photodegradation of partially hydrolysed polyacrylonitrile (HPAM) after 180 min under UV light irradiation in a suspension photocatalytic reactor. The results suggest that the photodegradation of HPAM contaminant by GCN/PAN nanofibres was due to the synergetic effects of HPAM adsorption by the PAN nanofibres and HPAM photodegradation by the GCN. This study provides an insight into the removal of HPAM from polymer-flooding produced water (PFPW) through photocatalytic degradation of liquid-permeable self-supporting nanofibre mats as a potentially promising material to be used in industrial applications