Ethical Work Climate, Social Trust, and Decision-Making in Malaysian Public Administration: The Case of MECD Malaysia

This paper examines the role of social trust in influencing ethical work climate and decision-making. Moderated regression analysis was used to analyse the data. A survey was carried out at the Ministry of Entrepreneur and Cooperative Development (MECD) in Malaysia, and was completed by all 349 employees, which permitted a comprehensive overview. We found that company interest, friendship, team play, and personal morality were closely related to increasing social trust (p <0.05). Social trust also mediated a positive impact of company interest, friendship, team play, and personal morality on decision-making with p <0.05. Rules and procedures had no significant impact either on social trust or decision-making. Eight hypotheses were confirmed, while two were rejected. Implications for practice and research are discussed

Wireless power transmission - exploring source to load inductive link under resonance and varying load condition

Wireless Power Transmission for powering up of the electronic gadgets, electric vehicles, and biomedical implants is being researched heavily these days. It has been proving to be the only in the electric vehicles battery charging systems, as it is hassle free, more efficient and easy to install. In many-to-one as well in one-to-many power transfer selective resonant technique plays a vital role. The many-to-one principle is mainly in the battery charging system of electric vehicles. This paper explores the source-to -load-coil (one -toone and one-to-two) links using the magnitude of the reflective impedance (ZRef) as a parameter estimating the power transfer efficiency. The analytic expressions and simulation results have been explored in this analysis, showing the effect of resonant and load matching

Application of Particle Swarm Optimization in Optimizing Stereo Matching Algorithm’s Parameters for Star Fruit Inspection System

This paper reports the finding of the experimentation of the Particle Swarm Optimization in optimizing the stereo matching algorithm’s parameters for the star fruit inspection system. The star fruit inspection system is built by CvviP Universiti Teknologi Malaysia. While the stereo matching algorithm used in the experiment is taken from the Matlab library. Each particle of Particle Swarm Optimization in the search pace repsents a set of candidate numerical value of the stereo matching’s parameters. The fitness function for this application is the sum of absolute error of the gray scale value of both images. Based on this information, the particles will improve its position in the search space by moving towards its best record and the swarm best record. The process repeated until the maximum iteration met. The result indicates that there is potential application of Particle Swarm Optimization in stereo matching’s parameters tuning

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

An Experimental Study of the Application of Gravitational Search Algorithm in Solving Route Optimization Problem for Holes Drilling Process

Previously, route planning in holes drilling process has been taken for granted due to its automated process, in nature. But as the interest to make Computer Numerical Control machines more efficient, there have been a steady increase in number of studies for the past decade. Many researchers proposed algorithms that belong into Computational Intelligence, due to their simplicity and ability to obtain optimal result. In this study, an optimization algorithm based on Gravitational Search Algorithm is proposed for solving route optimization in holes drilling process. The proposed approach involves modeling and simulation of Gravitational Search Algorithm. The performance of the algorithm is benchmark with one case study that had been frequently used by previous researchers. The result indicates that the proposed approach performs better than most of the literatures

Portable data acquisition and fluidic system for electrochemical Sensors

The recent outbreak of infectious diseases has highlighted the necessity of point-of-care detection compared to central lab analysis for more effective epidemic control. Recent developments in the field of biosensors have allowed sensitive, accurate disease diagnosis using low-cost devices. In this work, we describe the development of a portable data acquisition and fluidic system for miniature electrochemical biosensors. The data acquisition system was designed as a single printed circuit board and can perform cyclic voltammetry. The fluidic chamber was designed to work with three miniature sensors which are placed on a single platform. Leakage tests were performed to ensure that each chamber allows sensor isolation and avoids any cross- contamination. Measurements using the fabricated potentiostat board were taken and compared with a commercial potentiostat. It was found that the designed potentiostat was able to measure the same resolution and peak separation in cyclic voltammetry measurements

Classification System for Wood Recognition using K-Nearest Neighbor with Optimized Features from Binary Gravitational Algorithm

Woods species recognition is a texture classification difficulty that has been studied by many researchers years ago. The species of the wood are identified by the proposed classification using the textural type that can be observed on the structural features for example the colour of the woods, weight, texture and other features. Any mistakes on texture recognition will affect the economic benefits of wood production where it is an important basis for an identification of woods. Besides, to guide a person to be skilled in wood recognition, it will take a long time and the result the wood sample can be biased. These kinds of problem can be a motivation to develop a system that can recognize the wood effectively. This project will try to integrate both attempts by proposing a classification system consists of feature extractor, classifier and optimizer. The project proposes a classification system using Gray Level Co-Occurrence Matrix (GLCM) as feature extractor, K-Nearest Neighbor (K-NN) as classifier and Binary Gravitational Search Algorithm (BGSA) as the optimizer for GLCM’s feature selection and parameters. For this project, images of wood knot from CAIRO UTM database are used for benchmarking the proposed system performance. The result shows that the proposed approach can perform as good as previous literatures with fewer features used as input for the classifier

Magnetic Optimization Algorithm Approach For Travelling Salesman Problem

Lately, numerous nature inspired optimization techniques has been applied to combinatorial optimization problems, such as Travelling Salesman Problem. In this paper, we study the implementation of one of the nature inspired optimization techniques called Magnetic Optimization Algorithm in Travelling Salesman Problem. In this implementation, each magnetic agent or particle in Magnetic Optimization Algorithm represents a candidate solution of the Travelling Salesman Problem. The strength of the magnetic force between these particles is inversely proportion to the distance calculated by the Traveling Salesman Problem's solution they represented. Particles with higher magnetic force will attract other particles with relatively lower magnetic force, towards it. The process repeated until satisfying a stopping condition, and the solution with lowest distance is considered as the best-found solution. The performance of the proposed approach is benchmarked with a case study taken from a well-known test bank

Clinical performance of reverse transcription loop mediated isothermal amplification COVID-19 assay on gold- nanoparticle-modified screen-printed Carbon Electrode using differential pulse voltammetry

The World Health Organization (WHO) has recommended real-time reverse transcription polymerase chain reaction (RT-PCR) as the gold standard for coronavirus disease detection. In this study, we aim to validate the clinical performance of reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay on gold-nanoparticle-modified screen-printed carbon electrode (AuNP/SPCE) using differential pulse voltammetry (DPV) and to compare it with real-time RT-PCR. The shape of the electrodeposited AuNP on SPCE was quasi-spherical with a size of ±500 nm. The developed RT-LAMP primer was designed from the GenBank database using the NCBI Multiple Alignment tools and Jalview software. Nasopharyngeal clinical samples were obtained from suspected COVID-19 patients (n = 148). The RT-LAMP products were dropped on the modified AuNP/SPCE under DPV setting, which resulted in current change (∆I) responses. The positive and negative samples produced significantly different ∆I signals with a p-value <0.0001 at a 95% confidence interval using Student’s t-test. The RT-LAMP assay using Au/SPCE exhibited a 30 s response time per analysis. The clinical sensitivity and specificity obtained were 79.7% and 85.1%, respectively, with a detection limit of 0.4 copies µl−1. Hence, this proposed method is suitable for COVID-19 RNA detection in resource-limited settings