Application of e-marketing by small-medium housing developer companies in Kelantan, Malaysia.

Global competition among the players in the housing and construction industry has pushed the companies to be more surviving and struggling for their long term competitiveness and hence;identifying a more innovative marketing strategy. In this context, e-marketing has been considered as one of the main aspects of marketing strategies among the companies, whereby the internet as the main vehicle

The association of shift work and coronary heart disease risk factors among male factory workers in Kota Bharu, Kelantan

Shift work is one of the work hour systems in which a relay of employees extends the period of production beyond the conventional 8-hour working day. It has been found to be associated with various health problems and there is concern that shift workers are at higher risk to develop risk factors for coronary heart disease (CHD). The study was undertaken to examine relationships between shift work and CHD risk factors, namely hypertension, dyslipidaemia (either hypercholesterolaemia, hyper-low density lipoprotein-cholesterolaemia, hypo-high density lipoprotein-cholesterolaemia or hypertriglyceridaemia), high body mass index (BMI), hyperglycemia and physical inactivity among male factory workers in a factory in Kota Bharu, Kelantan. METHODS: This study was a contrived cross-sectional study of 76 shift and 72 day workers from one ofthe factories in Kota Bharu, Kelantan. Data was collected through a questionnaire on psychosocial and life-style factors, anthropometric and blood pressure measurement, fasting blood sugar and fasting lipid proJiles analyses. RESULTS: The prevalence of hypertension, hypercholesterolaemia, hypertriglyceridaemia and high body mass index (BMO were significantly higher among shift workers compared to day workers. There was no difference in the prevalence of hyperg[ycemia, hypo-high-density lipoprotein-cholesterolaemia, hyper-high-density lipoprotein-cholesterolaemia and physical inactivity. When the shiji workers were compared with the day workers, the aajusted odds ratio (OR) for hypertension, high BMI andphysical inactivity were 9.1 (95% CI 1.4-56.8), 2.9 (95% CI 1.3-6.1) and 7.7 (95% CI 2.1-27.5) respectively. There was neither association of shift work with dyslipidaemia, nor with hyperglycemia. CONCLUSIONS: There were positive association between shiji work and hypertension, high BMI andphysical inactivity which denotes a higher risk of CHD risk factors among shift workers compared to day workers

Sistem pemantauan transformer dengan IoT

Artikel ini sebagai laporan untuk kajian yang berkaitan transformer dan sistem pemantauan masa nyata. Dalam dekad yang lalu, banyak perhatian dibuat untuk memperkenalkan sistem pintar dan peralatan untuk memenuhi keperluan semasa dan menjadikan kehidupan selesa. Satu kepentingan baru-baru ini dalam komunikasi Mesin ke Mesin dikenali sebagai Internet of Things (IOT), untuk membolehkan peranti autonomi menggunakan Internet untuk bertukar data. Kerja-kerja ini membentangkan reka bentuk dan pelaksanaan pemantauan masa nyata dan pengesanan kesalahan pengubah dan rekod penunjuk operasi utama pengubah penyebaran seperti beban semasa, voltan, minyak pengubah dan merangkumi suhu dan kelembapan. Transformer adalah salah satu peralatan elektrik yang paling penting yang digunakan dalam sistem penghantaran kuasa kerana mereka melaksanakan fungsi mengubah tahap voltan. Oleh itu, penyelenggaraan pengubah kuasa adalah wajib; kerana ia terletak di kawasan geografi yang berbeza, pemantauan berkala tidak mungkin sepanjang masa disebabkan oleh tenaga manusia yang tidak mencukupi. Sekiranya ada sesuatu yang tidak normal atau keadaan kecemasan berlaku, sistem itu boleh dipantau melalui internet yang mengandungi maklumat tentang ketidaknormalan mengikut beberapa arahan yang telah ditetapkan yang diprogramkan dalam mikrokontroler. Sistem ini akan membantu transformer untuk beroperasi dengan lancar dan mengenal pasti masalah sebelum sebarang kegagalan bencana

CSLM: Levenberg Marquardt based Back Propagation Algorithm Optimized with Cuckoo Search

Training an artificial neural network is an optimization task, since it is desired to find optimal weight sets for a neural network during training process. Traditional training algorithms such as back propagation have some drawbacks such as getting stuck in local minima and slow speed of convergence. This study combines the best features of two algorithms; i.e. Levenberg Marquardt back propagation (LMBP) and Cuckoo Search (CS) for improving the convergence speed of artificial neural networks (ANN) training. The proposed CSLM algorithm is trained on XOR and OR datasets. The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study

Dinar and dirham: a study on the standard measure value of goods and services during the Prophet Muhammad’s (pbuh) era

This study discusses the appropriateness of the gold dinar and silver dirham as a measure of the value of goods and services. According to Islamic history, this precious metal has been used as a measure of value long before the period of Prophet Muhammad (pbuh). So, the research question here is how the two metals had been applied as a measure of value at the time of Prophet Muhammad (pbuh). Qualitative data collection techniques and data analysis used in this study through written materials that the Prophet Muhammad himself (al-Hadith). Finally, the results showed that the two metals are protect the price value (non-inflation) and suitable as a standard measure of the value of goods and services of all time for the welfare and prosperity of all mankind

The Application of Water Cooling on Reducing NOx from a Gas Burner System

A gas burner system applying water cooling has been investigated using a 140mm inside diameter combustor of 294-mm length. The combustor was placed vertically upwards. All tests were conducted using natural gas only. A fixed straight blade radial swirler with 76-mm outlet diameter was placed at the inlet plane of the combustor. An orifice plate of 59 mm was inserted at the exit plane of the swirler to enhance turbulence and help in mixing of the fuel and air. Fuel was injected at the back plate of the swirler using central fuel injector with eight fuel holes pointed radially outward. Tests were conducted at 5mmH 20 pressure loss. A reduction of about 21.53% on NOx emissions was achieved at equivalence ratio of near stoichiometric (0.88) and a reduction of 35.7% was achieved at equivalence ratio of 0.42. Other emissions such as carbon monoxide were well under 100 ppm except below the equivalence ratio of 0.5 due to cooling effect. Unburned hydrocarbon emissions were well below 10 ppm except below the equivalence ratio of 0.5

Self-adaptive Based Model for Ambiguity Resolution of The Linked Data Query for Big Data Analytics

Integration of heterogeneous data sources is a crucial step in big data analytics, although it creates ambiguity issues during mapping between the sources due to the variation in the query terms, data structure and granularity conflicts. However, there are limited researches on effective big data integration to address the ambiguity issue for big data analytics. This paper introduces a self-adaptive model for big data integration by exploiting the data structure during querying in order to mitigate and resolve ambiguities. An assessment of a preliminary work on the Geography and Quran dataset is reported to illustrate the feasibility of the proposed model that motivates future work such as solving complex query

WCBP: A new water cycle based back propagation algorithm for data classification

Water Cycle algorithm is a modern nature inspired meta-heuristic algorithm to provide derivative-free solution to optimize complex problems. The back-propagation neural network (BPNN) algorithm performs well on many complex data types but it possess the problem of network stagnancy and local minima. Therefore, this paper proposed the use of WC algorithm in combination with Back-Propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory. The performance of the proposed Water Cycle based Back-Propagation (WCBP) algorithm is compared with the conventional BPNN, ABC-BP and ABC-LM algorithms on selected benchmark classification problems from UCI Machine Learning Repository. The simulation results show that the BPNN training process is highly enhanced when combined with WC algorithm

BAT-BP: A new BAT based back-propagation algorithm for efficient data classification

Training neural networks particularly back propagation algorithm is a complex task of great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. Thus, this study investigates the use of Bat algorithm along with back-propagation neural network (BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to enhance the convergence rate. This study intends to show the superiority (time performance and quality of solution) of the proposed meta-heuristic Bat-BP algorithm over other more standard neural network training algorithms. The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. Classification datasets from UCI machine learning repository are used to train the network. The simulation results show that the efficiency of BPNN training process is highly enhanced when combined with BAT algorithm