This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic -rung orthopair fuzzy set (PL-ROFS) to handle the inherent uncertainties associated with astronomical data. The PL-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously.
View Article and Find Full Text PDFCircular Economy (CE) plays a crucial role in Latin America, where the transition to new economic development models poses significant challenges. This study conducts a bibliometric analysis of CE research in the region to identify critical areas of development, influential authors, organizations, and future research trends. This analysis aims to highlight the progress made in the CE field in Latin America and identify areas for improvement to promote sustainable development.
View Article and Find Full Text PDFDuring extreme events such as tropical cyclones, the precision of sensors used to sample the meteorological data is vital to feed weather and climate models for storm path forecasting, quantitative precipitation estimation, and other atmospheric parameters. For this reason, periodic data comparison between several sensors used to monitor these phenomena such as ground-based and satellite instruments, must maintain a high degree of correlation in order to issue alerts with an accuracy that allows for timely decision making. This study presents a cross-evaluation of the radar reflectivity from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement Mission (GPM) and the U.
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