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for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors.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

Performance of modified non-linear shooting method for simulation of 2nd order two-point BVPS

In this research article, numerical solution of nonlinear 2nd order two-point boundary value problems (TPBVPs) is discussed by the help of nonlinear shooting method (NLSM), and through the modified nonlinear shooting method (MNLSM). In MNLSM, fourth order Runge-Kutta method for systems is replaced by Adams Bashforth Moulton method which is a predictor-corrector scheme. Results acquired numerically through NLSM and MNLSM of TPBVPs are discussed and analyzed. Results of the tested problems obtained numerically indicate that the performance of MNLSM is rapid and provided desirable results of TPBVPs, meanwhile MNLSM required less time to implement as comparable to the NLSM for the solution of TPBVPs

Analysis and Development of the Generic Maintenance Management Process Modeling for the Preservation of Heritage School Buildings

Preservation of heritage school buildings requires special maintenance management practices. A thorough understanding of the maintenance management process is essential in ensuring effective maintenance practices can be instituted. The aim of this paper was to develop a generic process model that will promote the understanding of an effective management of maintenance process for heritage school buildings. A process model for the Maintenance Management of Heritage School Buildings (MMHSB) was developed using the Integration Definition for Function Modeling (IDEF0) system through an iterative process. The initial MMHSB process model was submitted to a team of management experts from the Malaysian Ministry of Arts and Heritage and the Ministry of Education Malaysia for verifications. Based on their feedbacks the initial model was refined and a proposed model was developed. From the second verification, the feed back received formed the basis for the final model. The final model elucidates the items for the input, mechanism, control and output elements that are critical in the maintenance management of heritage school buildings. The model also redefines the existing scope of responsibilities of the Headmasters’ and Senior Assistants’ in the management of maintenance. The perceived effectiveness of the model by potential users was surveyed using a selected number of administrators from potentially recognized heritage schools. The results indicated that the process model is perceived as being helpful in clarifying the maintenance management process of heritage school buildings and is useful in changing the current reactive management practices to that of a more proactive practice. In conclusion, it is believed that the MMHSB Process Model is helpful in promoting the understanding of the maintenance management process which would lead to improve preservation practices of heritage school buildings

Bio-polishing sludge adsorbents for dye removal

The objective of this work is to evaluate the removal of methylene blue dye by bio-polishing sludge-based adsorbents. The adsorbents were characterized according to the specific surface area, pH upon the treatment and surface functional groups. The adsorption of dye was carried out at room temperature, and the adsorption data were analyzed using the isotherm and kinetics models. The bio-polishing sludge is rich in ash content, and the presence of surface functional groups varied with the treatment strategies. The specific surface area of adsorbents is between 7.25 and 20.8 m2/g. Results show that the maximum removal of methylene blue by sludge adsorbents was observed to have the following order: untreated sludge (SR) > zinc chloride-treated (SZ) > microwave-dried (SW) = potassium carbonate-treated (SK) > acid-washed (SH). The maximum adsorption capacities for SR and SZ as predicted by the Langmuir model are 170 and 135 mg/g, respectively. Although SR demonstrates a higher maximum removal than SZ, the latter exhibits greater removal intensity and rate constant even at high dye concentration. The bio-polishing sludge is a promising adsorbent for dye wastewater treatment

Experimental of surface roughness and tool wear on coolant condition technique using Aluminium alloy 319 used in automotive industries

The present day the applications of machining part tolerances, like the automotive industries aimed to reduce the fuel consumption of their vehicle by reducing the total mass per vehicle and the method process for machining. Understanding of the interaction and significance machining parameters are important to improve the efficiency of any machining process and the accuracy part produced. The objective of this research is to analyze the machining parameters (spindle speed, depth of cut and feed rates) in a three machining conditions (dry, wet and 1.0 mm coolant nozzle size on the surface roughness and tool wear using Respond Surface Method (RSM) on the CNC Lathe machine with 2 axes movements. The synthetic soluble oils, and coated cemented carbide Al2 O3 insert were used as a workpiece material and cutting tool respectively. The result of the machining experiment for Aluminum alloy 319 was investigated to analyze the main factor affecting surface roughness using the analysis of Variance (ANOVA) method. The optimum selection of the cutting conditions effectively contributes to the increase in the productivity and reduction in the production cost; therefore almost attention is paid to this problem. In cutting process, optimization of cutting parameters is considered to be a vital tool for improvement in output quality of a product as well as reducing the overall production time. The acquired results showed that the coated cemented carbide Al2 O3 insert gives the optimum overall performance in terms of surface roughness and tool wear with the smallest orifice size coolant. The research also beneficial in minimizing the costs incurred and improving productivity of manufacturing firms using the mathematical model and equations, generated by CCD based on RSM method

Interference temperature measurements and spectrum occupancy evaluation in the context of cognitive radio

This paper presents a refined radio spectrum measurement platform specifically designed for spectrum occupancy surveys in the context of Cognitive radio. Cognitive radio permits the opportunistic usage of licensed bands by unlicensed users without causing harmful interference to the licensed user. In this work, a study based on the measurement of the 800 MHz to 2.4 GHz frequency band at two different locations inside Universiti Teknologi Malaysia (UTM), Johor Bahru campus, Malaysia is presented. Two Tektronix RSA306B spectrum analyzer are set up to conduct simultaneous measurements at different locations for a 24 hours period. The analysis conducted in this work is based on the real spectrum data acquired from environment in the experimental set up. Busy and idle channels were identified. The channels subject to adjacent-channel interference were also identified, and the impact of the detection threshold used to detect channel activities was also discussed. The consistency of the observed channel occupation over a range of thresholds and a sudden drop has good characteristics in determining an appropriate threshold needed in order to avoid interference

Digitizing radiology films using flat-bed scanner and produce a multimedia digital teaching file in musculoskeletal radiology

Digital images have been long established in radiology department. Despite this, most radiology teaching films has been film-based. The drawback of this conventional system is that it consumed space, only one user can use at any one time and expensive. Analogue radiology films were digitized using flatbed scanner with transparency adaptor and digital camera. Digital-based teaching files using personal computer (PC) should be encouraged as this will reduce space for storing, can be distributed without much cost, inexpensive, many users can use at the same time and others can use even though at different place using the internet. The teaching file was prepared using HyperText Mark-up Language (HTML) as this can be used using any web browser. It is now available in University Sains Malaysia, Kelantan Campus Branch (USMKCK) intranet

Space-times which are asymptotic to certain Friedman-Robertson-Walker space-times at timelike infinity

We define space-times which are asymptotic to radiation dominant Friedman-Robertson-Walker space-times at timelike infinity and study the asymptotic structure. We discuss the local asymptotic symmetry and give a definition of the total energy from the electric part of the Weyl tensor.Comment: 8 pages, Revte