Using critical slowing down indicators to understand economic growth rate variability and secular stagnation

Assessing the impact of COVID-19 and safety parameters on energy project performance with an analytical hierarchy process

COVID-19 has destabilized the global economy, disrupted the lives of billions of people globally, and caused the workforce to suffer. Furthermore, the spread of this disease has caused most nations to impose strict lockdown regulations and shutdown most industries. This study aimed to highlight the key issues of energy project performance alongside construction activities that were halted during the COVID-19 outbreak to follow social distancing, lockdown, and public safety parameters. A questionnaire survey was administered to accomplish the purpose of this study. The responses of 42 energy project professionals and experts were evaluated using the analytical hierarchy process (AHP) for group decision-making. AHP shows that the biggest influences on project performance during COVID-19 pandemic were government measures and personal factors. The findings provide insight to support energy project planning and management during and after the pandemic, including prioritization of labor force health and safety.

Using persistent homology as preprocessing of early warning signals for critical transition in flood

Flood early warning systems (FLEWSs) contribute remarkably to reducing economic and life losses during a flood. The theory of critical slowing down (CSD) has been successfully used as a generic indicator of early warning signals in various fields. A new tool called persistent homology (PH) was recently introduced for data analysis. PH employs a qualitative approach to assess a data set and provide new information on the topological features of the data set. In the present paper, we propose the use of PH as a preprocessing step to achieve a FLEWS through CSD. We test our proposal on water level data of the Kelantan River, which tends to flood nearly every year. The results suggest that the new information obtained by PH exhibits CSD and, therefore, can be used as a signal for a FLEWS. Further analysis of the signal, we manage to establish an early warning signal for ten of the twelve flood events recorded in the river; the two other events are detected on the first day of the flood. Finally, we compare our results with those of a FLEWS constructed directly from water level data and find that FLEWS via PH creates fewer false alarms than the conventional technique.