Abstract Aedes mosquitoes, the primary vectors of dengue, yellow fever, chikungunya, and Zika, remain a significant public health threat despite ongoing control efforts. Their adaptability to environmental changes has contributed to the continued rise in disease incidence. This study aims to evaluate and update the oviposition preferences of Aedes albopictus with respect to ovitraps colour and water sources across various sites at Universiti Sains Malaysia. The experiment tested five ovitraps colours: black, blue, green, orange, and red, alongside four water sources: rainwater, pond water, seasoned tap water, and distilled water. Over the study period, 2,640 eggs were collected, revealing significant differences (P<.05) in oviposition preferences for both ovitraps color (P = 0.012) and water source (P = 0.049). Black ovitraps attracted the highest number of eggs (672), followed by red (609), orange (410), blue (233), and green (190). Among the water sources, rainwater was most preferred (788 eggs), followed by pond water (725), seasoned tap water (630), and distilled water (497). Location 3 (School of Biological Sciences) recorded the highest egg count (972 eggs), although location differences were not statistically significant (P=0.434). Correlation analysis indicated a positive association between mosquito oviposition and water temperature (rs = 0.638; P = 0.047, P< .05). These findings highlight the importance of considering both ovitraps’ colour and water source in mosquito surveillance and control strategies, as these factors significantly influence the oviposition behaviour of Ae. albopictus. The results contribute valuable insights into optimizing ovitrap designs and selecting suitable water sources for more effective mosquito management in urban and suburban environments.

Abstract Controlling the power level in the TRIGA PUSPATI Reactor (RTP) is crucial for both producing accurate power output and managing reactor activity and power distribution. Currently, the RTP uses a Feedback Controller Algorithm (FCA) based on a Proportional-Integral (PI) controller to improve steady-state error during operation. However, this existing model faces issues such as delays in reaching a steady state and an inability to minimize errors due to insufficient power accuracy and an ineffective controller. To address these issues, a new structure called the Fractional Order Lead-Lag Compensator (FOLLC) has been introduced. Traditionally, the FOLLC structure is identified through loop shaping using Bode plots and root locus in the frequency response domain. In this study, however, the Particle Swarm Optimization (PSO) technique has been employed to estimate the values of the compensator’s poles and zeros. Integrating the compensator with the PSO approach improved the reactor core system's ability to reach and maintain the desired power output while minimizing deviations from the target power level, achieving Residual Mean Percentage (RMP) values between 0.75% and 2.35%. In comparison, the model without a compensator had much higher RMP values of 3.45% to 27.48%, showing a less accurate match with the real plant. This integration enhanced the overall performance of the reactor core system.