Radio refractivity chaos analyzed via #recurrence_quantification_analysis; humid stations showed higher complexity than semi-arid ones, affecting microwave link predictability seasonally
https://www.sciencedirect.com/science/article/pii/S0273117726001365
Not Earth-like yet Temperate? More Generic Climate Feedback Configurations Still Allow Temperate Climates in Habitable Zone Exo-Earth Candidates, Langbert, Chaucer, Apai, DΓ‘niel
A new method based on the Recurrence Quantification Analysis (RQA) of the heart rate (HR) offers an objective, efficient alternative to traditional methods for Aerobic Threshold (AerT) identification that have practical limitations due to the complexity of equipment and interpretation. This study aims to validate the RQA-based methodβs applicability across varied demographics, exercise protocols, and health status. Data from 123 cardiopulmonary exercise tests were analyzed, and participants were categorized into four groups: athletes, young athletes, obese individuals, and cardiac patients. Each participantβs AerT was assessed using both traditional ventilatory equivalent methods and the automatic RQA-based method. Ordinary Least Products (OLP) regression analysis revealed strong correlations (r > 0.77) between the RQA-based and traditional methods in both oxygen consumption (VO2) and HR at the AerT. Mean percentage differences in HR were below 2.5%, and the Technical Error for HR at AerT was under 8%. The study validates the RQA-based method, directly applied to HR time series, as a reliable tool for the automatic detection of the AerT, demonstrating its accuracy across diverse age groups and fitness levels. These findings suggest a versatile, cost-effective, non-invasive, and objective tool for personalized exercise prescription and health risk stratification, thereby fulfilling the studyβs goal of broadening the methodβs applicability.
To achieve a non-destructive, high-sensitivity, and in situ characterization of the dislocation density in metals, an integrated strategy is proposed based on nonlinear ultrasonic (NLU) and recurrence quantification analysis (RQA) techniques. Acoustic nonlinearity generated by dislocation-induced finite-amplitude ultrasonic distortion is analyzed in a chaotic behavior. The decreasing dislocation density in annealed pure iron samples is confirmed by transmission electron microscopy and X-ray diffraction analysis, as well as the densities for edge and screw types, respectively. They are positively correlated with a recurrence rate-based NLU parameter, Ξ²RQA, which is more effective in discriminating slight variations than the traditional NLU parameter Ξ², with an increased sensitivity of 43%. The NLU response under different fractions of edge dislocation is numerically calculated, and a noticeable difference is observed compared with the experiments. A stress-modified dislocation model for harmonics generation is then put forward on the basis of the weakened long-range and short-range interactions of dislocations in the annealing system. The proposed integrated strategy can provide a better insight into the quantitative diagnosis of dislocation density in metals.
Advances in computer vision technology have expanded the possibilities to facilitate complex task automation for integration into large-scale data processing solutions. Despite these advances, however, there is still a need to develop simple and efficient algorithms for image feature extraction and classification to enable easier and faster implementation into real-world applications. Here, a new method is described to extract features from images that can be used for image classification. It uses a fuzzy c-means (FCM) clustering-based approach that allows for unique object patterns to be spatially re-mapped onto a binary sparse matrix with which principles from recurrence quantification analysis statistics (RQAS) can be applied. RQAS are computationally efficient and can be used to create a short feature vector for effective binary and multi-class image classification. The utility of this method is demonstrated using both simulated and real datasets that include objects embedded in complex backgrounds, and is compared with another widely used and highly effective thresholding feature extraction method (local binary patterns (LBP)). Results show that the FCM-RQAS method described here can perform as well or better than LBP and supports the use and further development of RQAS-based image feature extraction for computer vision applications.
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