High Voltage Durability of Bambusa Vulgaris as a Bio-composite Material

This study is conducted in order to measure and identify the ability of a bio-composite material to the high voltage. According to it, the developed bio-composite material is tested to ensure the maximum voltage that the material can hold. The bio-composite material which made from a mixture of Bambusa Vulgaris and a selected polymer named as High Density Polyethylene (HDPE). The Bambusa Vulgarisis going through several processes before mixed together with HDPE using wood plastic composite (WPC) technique which also consists of several stages. There are several samples of bio-composite substance are fabricated. The difference among them is the composition of the raw materials (Bambusa Vulgaris and HDPE) used. In this research, the high voltage measurement which also called as breakdown voltage measurement of the bio-composite material is examined by using appropriate experiments. All the experimental results are presented and discussed in this paper

Social learning approach in designing persuasive e-commerce recommender system model

Intention to purchase in existing online business practice is learned through observation of information display by online seller. The emergent growth of persuasive technologies currently holds a great potential in driving a positive influence towards consumer purchase behavior. But to date, there is still limited research on implementing persuasion concept into the recommender system context. Drawing upon the principle design of persuasive system, the main purpose of this study is to explore social learning advantages in creating persuasive features for E-Commerce recommender system. Based on Social Cognitive Theory, the influence of personal and environmental factors will be examined in measuring consumer purchase intention. In addition, dimensions of social learning environment are represented by observational learning theory and cognitive learning theory. From those reviews, this study assumed that social learning environment can be created based on attentiveness, retentiveness, motivational, knowledge awareness and interest evaluation cues of consumer learning factors. Furthermore, the persuasive environment of recommender system is assumed to have positive influence towards individual characteristics such as self-efficacy behavior, perceived task complexity and confused by over choice. Findings from those reviews have contributed to the development of a research model in visualizing social learning environment that can be used to develop a persuasive recommender system in E-Commerce and hence measures the impact towards consumer purchase intention

Electrooxidation of nitrite ions on gold/polyaniline/carbon paste electrode

Nitrite ions can penetrate from fertilizers into underground water and consequently contaminate the water and food sources. A facile two-step electrochemical method was used to fabricate gold/polyaniline/carbon paste electrode (Au/PAni/CPE) for nitrite sensing. The Au/PAni/CPE was visualized and characterized by scanning electron microscopy, energy-dispersed X-ray spectroscopy, X-ray diffraction and electrochemical methods. The electrocatalytic activity of bare CPE, PAni/CPE and Au/PAni/CPE toward the electrooxidation of nitrite was examined and compared via cyclic voltammetry. To obtain the optimal condition for fabrication of the electrode, the number of cycles in cyclic voltammetry for synthesis of polyaniline and the deposition time in potentiostatic deposition of gold were optimized with respect to the electrooxidation of nitrite. In a phosphate buffer solution (PBS, pH 7.0), the peak current was linear to the concentration of nitrite in the range from 3.8×10-5 M to 1.0×10-3 M with a detection limit of 2.5×10-5 M. The interference effect on the nitrite detection was also studied. The proposed method was also employed for the determination of nitrite in rain and lake water samples

A Markovian Approach to Determine Optimal Means for SME Production Process

The determination of optimum process mean has become one of the focused research area in order to improve product quality. Depending on the value of quality characteristic, an item can be reworked, scrapped or accepted by the system which is successfully transform to the finishing product by using the Markovian model. By assuming the quality characteristic is normally distributed, the probability of rework, scrap and accept is obtained by the Markov model and next the optimum of process mean is determine which maximizes the expected profit per item. In this paper, we present the preliminary analysis of selecting the process mean by referring to SME production process. By varying the rework and scrap cost, the analysis shows the sensitivity of the Markov approach to determine process mean

Neural Network Prognostics Model for Industrial Equipment Maintenance

This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of equipment. By having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the industrial equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from industrial equipment and prognostics approach

Applied Markovian Approach for Determining Optimal Process Means in Single Stage SME Production System

The determination of optimum process mean has become one of the focused research area in order to improve product quality. Depending on the value of quality characteristic of juice filling in the bottle, an item can be reworked, accepted or accepted with penalty cost by the system which is successfully transform to the finishing product by using the Markovian model. By assuming the quality characteristic is normally distributed, the probability of rework, accept and accept with penalty cost is obtained by the Markov model and next the optimum of process mean is determined which maximizes the expected profit per item. In this paper, we present the analysis of selecting the process mean in the filling process. By varying the rework and accept with penalty cost, the analysis shown the sensitivity of the Markov approach to determine the process mean

Estimation of Photovoltaic Module Parameters based on Total Error Minimization of I-V Characteristic

Mathematical Modelling of photovoltaic (PV) modules is important for simulation and performance analysis of PV system. Therefore, an accurate parameters estimation is necessary. Single-diode and two-diode model are widely used to model the PV system. However, it required to determine several parameters such as series and shunt resistances that not provided in datasheet.  The main goal of PV modelling technique is to obtain the accurate parameters to ensure the I-V characteristic is closed to the manufacturer datasheet. Previously, the maximum power error of calculated and datasheet value are considered as objective to be minimized for both models. This paper proposes the PV parameter estimation model based minimizing the total error of open circuit voltage (VOC), short circuit current (ISC) and maximum power (PMAX) where all these parameters are provided by the manufacturer. The performance of single-diode and two-diode models are tested on different type of PV modules using MATLAB. It found that the two-diode model obtained accurate parameters with smaller error compared to single-diode model. However, the simulation time is slightly higher than single-diode model due extra calculation required

Optimization of LRT Route for Mobile Web Application Engine

Integrated Light Rail Transit (LRT) System in the capital of Malaysia connects some key districts to historical places, interesting places, business areas and shopping malls are concentrated. The train services are running independently but have interchanges to integrate from one different LRT lines. This may leads the traveler facing difficulty when they are choosing incorrect destination station especially on different LRT lines which contribute to time consuming and high costing. In previous research we already implement the mobile web application architecture where the destination-oriented routes need to be dynamically generated by determining the nearest station according to the specific places. In this paper, we proposed the used of Dijkstra’s Algorithms to provide more effective and intelligent shortest path system to provide the solution for traveler to reach the desired destination

Ergogenic, anti-diabetic and antioxidant attributes of selected Malaysian herbs: characterisation of flavonoids and correlation of functional activities

In the present work, aqueous ethanolic (60% ethanol) extracts from selected Malaysian herbs including Murraya koenigii L. Spreng, Lawsonia inermis L., Cosmos caudatus Kunth, Piper betle L., and P. sarmentosum Roxb. were evaluated for their ergogenic, anti-diabetic and antioxidant potentials. Results showed that the analysed herbs had ergogenic property and were able to activate 5'AMP-activated protein kinase (AMPK) in a concentration dependant manner. The highest AMPK activation was exhibited by M. koenigii extract which showed no significant (p > 0.05) difference with green tea (positive control). For anti-diabetic potential, the highest α-glucosidase inhibition was exhibited by M. koenigii extract with IC50 of 43.35 ± 7.5 μg/mL, which was higher than acarbose (positive control). The determinations of free radical scavenging activity and total phenolics content (TPC) indicated that the analysed herbs had good antioxidant activity. However, C. caudatus extract showed superior antioxidant activity with IC50 against free radical and TPC of 21.12 ± 3.20 μg/mL and 221.61 ± 7.49 mg GAE/g, respectively. RP-HPLC analysis established the presence of flavonoids in the herbs wherein L. inermis contained the highest flavonoid (catechin, epicatechin, naringin and rutin) content (668.87 mg/kg of extract). Correlations between the analyses were conducted, and revealed incoherent trends. Overall, M. koenigii was noted to be the most potent herb for enhancement of AMPK activity and α-glucosidase inhibition but exhibited moderate antioxidant activity. These results revealed that the selected herbs could be potential sources of natural ergogenic and anti-diabetic/antioxidant agents due to their rich profile of phenolics. Further analysis in vivo should be carried out to further elucidate the mechanism of actions of these herbs as ergogenic aids and anti-diabetic/antioxidant agents