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Programmable Integrated Circuit (PIC) module for educational purposes is developed based on the existing unsatisfactory PIC simulation which is not able to achieve the objective and learning outcome in enhancing the student’s psychomotor aspect through circuit designing, installation and trouble shooting. The existing PIC programming through computer simulation, which students facing difficulty in observing and understanding the wiring assembly between the input module, PIC microcontroller and output modules. The development of PIC module has been designed and improved by increasing the number of output components,cost reduction and it is also user friendly. Furthermore, this PIC module is embedded with I/O module such as normally open push buttons, 12VDC motor, servomotor, LEDs light, LCD display, and seven-segment display. Newly developed PIC module can be programme and re-programme for various tasks. Based on students Lab Practical Assessment record, there is an average of 39% and 22% of improvement on knowledge and psychomotor skill respectively when students used newly developed PIC modules for laboratory practical work.

This study aimed to evaluate the effect of intravenous (IV) contrast media on semi quantification value during fluorine-18 fluorodeoxyglucose (18F-FDG) PET/CT in cancer imaging. We reviewed whole body 18F-FDG PET/CT scans of 51 oncology cases performed with and without IV contrast administration. Non contrast-enhanced CT (NECT) images were acquired following IV injection of FDG then followed by contrast-enhanced CT (CECT) images acquisition utilizing non-ionizing iodinated contrast media without positional change. The contrast injection was acquired using an automatic intravascular injection conversion kit (U.S. Patent No. 5,792,102, 1998). PET images were reconstructed using both NECT and CECT image datasets. Region of interest (ROI) was drawn on field of view over the heart, liver, spleen, inferior vena cava, psoas major muscle, urinary bladder and site of lesion on PET/CECT images. Similar ROI was copied to the corresponding PET/NECT images. The maximum standardized uptake values (SUVmax) of both datasets were compared using paired sample t-test with p < 0.05 considered as significant. The mean A± standard deviation percentage differences of SUVmaxbetween PET/CECT and PET/NECT for all investigated organs and lesions were not statistically significant (p > 0.05). Contrast-enhanced 18F-FDG PET/CT protocol did not cause significant effect in PET semiquantification uptake value, hence this protocol can be recommended in routine PET/CT examination, optimizing its role as a 'one-stop' imaging modality.

Research in prognosis maintenance, a branch of condition-based maintenance has received more attention from researchers lately. They focus on predicting when is the most suitable time to perform maintenance. Our review suggests that investigation on feature extraction in development of prognosis prediction model is still limited. This paper presents our study to find the most effective method for features extraction from maintenance monitoring data. The chosen features set should effectively improve the prognosis maintenance model performance. There have been several investigations to study feature extraction methods; however, the appropriate one is yet to be identified. In this research, we used datasets publicly available from National Aeronautics and Space Administration (NASA) army research laboratory. These datasets were generated through a simulation of the turbofan engine by using Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software developed by NASA army research laboratory. Features extraction methods such as correlation among sensors, correlation among the outputs, variable weighing and treated data methods were studied in this research. Next, the extracted features were applied to the regression tree for searching an appropriate prognosis model. Based on the Remaining Useful Life (RUL) prediction results, the correlation among sensors method was found as the best method that can represent the most useful features for the prediction model.