Commuting Accidents among Health Care Workers Working in Malaysia Government Hospitals

Commuting accidents are accidents occurring while travelling to and from work, and in the course of work. Studies have demonstrated that long working hours are associated with deprived sleeping time. Acute sleep deprivation may result in significant changes in cognitive functioning, alteration of mental status resembling depression or anxiety and difficulty with short-term memory. Some other studies have found that sleep deprivation significantly affects physician performance, alertness and patient safety. However, individual factors concerning workers’ behavior, family-related factors (parenting responsibility), work burden, workplace support as well as environmental factors such as bad weather and bad road conditions are also significant contributors of commuting accidents. The aim of this study is to investigate the relationship of long working hours or odd working hours and the risk exposure of the drivers during their work-commuting trips. The study was based on data collected from official notification forms. Sample size for this research was 554 based on 2014 to 2017 reported cases. Review of the statistics showed that most of the commuting accident causalitiesoccurred during travel to work (30.1%), compared to back from work after normal office hours (28.7%) and during odd hours (night shift and post-call) (12.5%). Nurses contributed significantly to these causalities (53%), followed by hospital attendants (17%), medical officer and assistant medical officer, respectively, 6 percent. Theincreasing number of commuting accidents among healthcare workers is alarming. As such, it is timely that proactive actions be taken by employers to educate their employees, their most valuable assets, on safe commuting management. Keywords: commuting accident, healthcare workers, road crashe

Prediction and optimisation of syngas production from air gasification of Napier grass via stoichiometric equilibrium model

Napier grass is a promising candidate as a potential solid biofuel due to its wide availability, high growth rate, carbon neutrality and high volatility. Syngas is produced from gasification of Napier grass which can be further utilised for production of renewable fuel and other chemicals. The quality of the syngas produced from gasification of Napier grass is dependent on various factors such as operating temperature and pressure, gasification medium, biomass versus air ratio and moisture content. The optimisation of process parameters is important due to productivity and economic reasons. Experimental investigations to determine optimum conditions for gasification process are cost intensive and time consuming, rendering these techniques to be impractical. Thus, in this study, a stoichiometric equilibrium model for simulation of air gasification of Napier grass is developed. The model is modified to include correction factors at a series of temperatures and ERs which are multiplied with equilibrium constants to improve the accuracy of the model in predicting syngas and carbon compositions. The predicted values are in good agreement with experimental measurement, validating the model as a reliable tool for simulation of gasification performance. The modified model is further utilised to determine optimum operating conditions for maximum hydrogen production

A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram

This paper presents a diagnostic analytics of harmonic source signature recognition of rectifier and inverter-based load in the distribution system with single-point measurement at the point of common coupling by utilizing Periodogram. Signature recognition pattern is used to distinguish the harmonic sources accurately by obtaining the distribution of harmonic and interharmonic components and the harmonic contribution changes.  This is achieved by using the significant signature recognition of harmonic producing load obtained from analysing the harmonic contribution changes. Based on voltage and current signature analysis, the distribution of harmonic components can be divided into three zones. To distinguish between the harmonic producing loads, the harmonic components are observed at these zones to get the signature recognition pattern. The result demonstrate that periodogram technique accurately diagnose and distinguish the type of harmonic sources in the distribution system

Improved energy conversion performance of a novel design of concentrated photovoltaic system combined with thermoelectric generator with advance cooling system

Most of the incident solar energy on a PV panel is converted into waste heat. This consequently reduces the efficiency of PV system. Therefore, if certain portion of this waste heat can be utilized adding a thermoelectric generator (TEG) in the PV panel endowed by an efficient cooling system, the output performance of the system can be improved significantly. In this study, a new configuration of nanofluid-based PV/T-TEG hybrid system with cooling channel is proposed to convert certain portion of waste heat to electrical energy in order to improve the overall efficiency of hybrid system. Thus, the nanofluid acts as a coolant and absorbs the heat from the back side of TEG module raising its gradient of temperature, as well as the overall performance of the system. Through a numerical modelling approach, performance of the proposed innovative design has been investigated and compared with the conventional solar-harvesting technology systems. At the optimum value of solar concentration C, and maximum operating temperature of 35°C, the obtained results reveal that the electrical energy in NCPV/T-TEG configuration has been found higher by 10%, 47.7% and 49.5% against NCPV/T, CPV and CPV/TEG-HS systems, respectively. Overall, the proposed design of NCPV/T-TEG hybrid system has potential for further development in high-concentration solar systems. © 2018 Elsevier Lt

Thermogravimetric study of Chlorella vulgaris for syngas production

The present study investigates the thermal degradation behavior of Chlorella vulgaris using a thermogravimetric analyzer (TGA) to explore application as feedstock for syngas production. The biomass was heated continuously from room temperature to 1000 °C at different heating rates (5, 10 and 20 °C min− 1) under N2/air conditions at a constant flow rate of 25 mL min− 1. Experimental results showed that the combustion process of C. vulgaris can be divided into three major phases; (1) moisture removal, (2) devolatilization of carbohydrates, protein and lipids and (3) degradation of carbonaceous material. A degradation rate of 80% was obtained at the second phase of the combustion process in the presence of air whilst a degradation rate of 60% was obtained under N2 atmosphere at the same phase. The biomass was further gasified for syngas production using a Temperature Programmed Gasifier (TPG). The effect of three different process variables, temperature, microalgal loading, and heating rate was investigated. The maximum H2 production was found at 800 °C temperature with a biomass loading of 0.5 g. No significant effect of heating rate was observed on H2 production. The activation energy values, based on the Kissinger method, were evaluated to be 45.38 ± 0.5 kJ mol− 1 (1st stage), 61.20 ± 0.5 kJ mol− 1 (2nd stage) and 97.22 ± 0.5 kJ mol− 1 (3rd stage). The results demonstrate a significant potential for the utilization of the microalgae biomass as feedstock for large-scale production of syngas via gasification

Smart Home Control for Disabled Using Brain Computer Interface

Electroencephalography (EEG) based smart home control system is one of the major applications of Brain Computer Interface (BCI) that allows disabled people to maximize their capabilities at home. A Brain Computer Interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. In this project, the scope includes Graphical User Interface (GUI) acts as a control and monitoring system for home appliances which using BCI as an input. Hence, NeuroSky MindWave headset is used to detect EEG signal from brain. Furthermore, a prototype model is developed using Raspberry Pi 3 Model B+, 4 channels 5V relay module, light bulb and fan. The raw data signal from brain wave is being extracted to operate the home appliances. Besides, the results agree well with the command signal used during the experiment. Lastly, the developed system can be easily implemented in smart homes and has high potential to be used in smart automation

Rehabilitation Of Ex-Mining Pond And Existing Wetland For Integrated Stormwater Management.

A study on rehabilitation of ex-mining pond and existing wetland for integrated storm water facilities has been carried out in Malaysia

Modelling and optimisation of biomass fluidised bed gasifier.

Recently, biomass for bioenergy and biofuel via gasification has become of great interest to energy and fuels production. Besides, gasification is recognised as a promising first processing step in an integrated biorefinery due to green and renewable technology. In this work, a stoichiometric equilibrium model of biomass fluidised bed gasifier is developed and followed by model improvement includes a correction factor to the equilibrium constants with a function of temperature. To illustrate the proposed model, bagasse is taken as the feedstock and gasification modelling based on the experiment result of a fluidised bed gasifier is presented. To ensure the accuracy of the model, predicted syngas compositions are validated with the experimental results. Besides, the proposed model is also reformulated for different types of biomass feedstock (e.g., rice husk, coconut shell, etc.). Based on the developed models, the operating condition of the gasifier can be optimised and the composition of the syngas can also be determined

Computational Fluid Dynamics Simulation of Gas-Solid Hydrodynamics in a Bubbling Fluidized-Bed Reactor: Effects of Air Distributor, Viscous and Drag Models

In this work, we employed a computational fluid dynamics (CFD)-based model with a Eulerian multiphase approach to simulate the fluidization hydrodynamics in biomass gasification processes. Air was used as the gasifying/fluidizing agent and entered the gasifier at the bottom which subsequently fluidized the solid particles inside the reactor column. The momentum exchange related to the gas-phase was simulated by considering various viscous models (i.e., laminar and turbulence models of the re-normalisation group (RNG), k-ε and k-ω). The pressure drop gradient obtained by employing each viscous model was plotted for different superficial velocities and compared with the experimental data for validation. The turbulent model of RNG k-Ɛ was found to best represent the actual process. We also studied the effect of air distributor plates with different pore diameters (2, 3 and 5 mm) on the momentum of the fluidizing fluid. The plate with 3-mm pores showed larger turbulent viscosities above the surface. The effects of drag models (Syamlal–O’Brien, Gidaspow and energy minimum multi-scale method (EMMS) on the bed’s pressure drop as well as on the volume fractions of the solid particles were investigated. The Syamlal–O’Brien model was found to forecast bed pressure drops most consistently, with the pressure drops recorded throughout the experimental process. The formation of bubbles and their motion along the gasifier height in the presence of the turbulent flow was seen to follow a different pattern from with the laminar flow.Ministry of Higher Education (MOHE) Malaysia; Engineering and Physical Sciences Research Counci