Illuminance level in the softcopy image viewing room is a very important factor to optimize productivity in radiological diagnosis. In today's radiological environment, the illuminance measurements are normally done during the quality control procedure and performed annually. Although the room is equipped with dimmer switches, radiologists are not able to decide the level of illuminance according to the standards. The aim of this study is to develop a simple real-time illuminance detector system to assist the radiologists in deciding an adequate illuminance level during radiological image viewing. The system indicates illuminance in a very simple visual form by using light emitting diodes. By employing the device in the viewing room, illuminance level can be monitored and adjusted effectively.
This study aims to investigate and establish a suitable model that can help to estimate aerosol optical depth (AOD) in order to monitor aerosol variations especially during non-retrieval time. The relationship between actual ground measurements (such as air pollution index, visibility, relative humidity, temperature, and pressure) and AOD obtained with a CIMEL sun photometer was determined through a series of statistical procedures to produce an AOD prediction model with reasonable accuracy. The AOD prediction model calibrated for each wavelength has a set of coefficients. The model was validated using a set of statistical tests. The validated model was then employed to calculate AOD at different wavelengths. The results show that the proposed model successfully predicted AOD at each studied wavelength ranging from 340 nm to 1020 nm. To illustrate the application of the model, the aerosol size determined using measure AOD data for Penang was compared with that determined using the model. This was done by examining the curvature in the ln [AOD]-ln [wavelength] plot. Consistency was obtained when it was concluded that Penang was dominated by fine mode aerosol in 2012 and 2013 using both measured and predicted AOD data. These results indicate that the proposed AOD prediction model using routine measurements as input is a promising tool for the regular monitoring of aerosol variation during non-retrieval time.
A three layer waveguiding silicon dioxide (SiO(2))/silicon nitride (Si(3)N(4))/SiO(2) structure on silicon substrate was proposed as an optically efficient biosensor for calibration of heavy metal ions in drinking water. The catalytic activities of urease and acetylcholine esterase (AchE) were inhibited by the presence of cadmium (Cd(2+)) and lead (Pb(2+)) ions. The detection limit as low as 1 ppb was achieved by employing the technique of total reflection at the interface between the Si(3)N(4) core and composite polyelectrolyte self-assembled (PESA) membranes containing cyclotetrachromotropylene (CTCT) as an indicator.