This study investigated coral and reef fish recovery following the COVID-19 event between low and high environmental disturbance reefs at Racha Yai Island, Southern Thailand. Three and four 50-m permanent line transects were set at low and high environmental reefs to collect the percent of live coral cover, fish diversity and abundance, and fish trophic-functional groups based on diet and habitat use. Our results showed a significant rise in the percentage of live coral cover, the number of individual fish, the number of fish species, and species richness at both bays following the COVID-19 lockdown due to a crucial reduction in human activities on the reef. In addition, there were increases in the number of corallivore fishes belonging to Chaetodontidae and Pomacentridae families and a reduction of omnivorous fish at the fish-feeding tourist attraction reefs due during the COVID-19 lockdown due to reducing fish-feeding tourism. This indicated that restricted human activities and reduced anthropogenic stress on a coral reef may have substantial short-term impacts on reef fish diversity. Our insights could help designate guidelines to manage tourist impacts on coral reefs and aid in their prolonged persistence. Doi: 10.28991/CEJ-2022-08-11-011 Full Text: PDF

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments.