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In this paper, we explore whether the activity of SARS-CoV-2 was associated with seasonality. MF-DFA model is utilized to calculate multifractal strength and multifractal complexity to evaluate the change state of SARS-CoV-2 activity. We select 10 countries with serious epidemic in the world, which are distributed in different latitudes of the northern and southern hemispheres. The study utilized the time series data of daily new cases and daily new deaths recorded in these countries. We regard May to October as the "high temperature season" for countries in the northern hemisphere, November to April as the "low temperature season", and the southern hemisphere is just the opposite. By comparing the multifractal intensity Δ H and multifractal complexity Δ α of the two time series in the two seasons, we draw a conclusion that, for both the sequence of the daily newly diagnosed persons and the daily newly increased number of deaths, in the countries of both the northern and southern hemispheres, Δ H and Δ α are weaker in the "low temperature season". That is, in the low temperature environment, SARS-CoV-2 can survive for a long time and be more infectious. In addition, we also observe that in the northern hemisphere, Iran is at a lower latitude, and although the SARS-CoV-2 activity in the low temperature season is higher than that in the high temperature season, the difference is not significant. Therefore, the lower latitude may resist this phenomenon. However, most of the countries in the southern hemisphere are within 30° of south latitude, with low latitude, and other meteorological characteristics such as humidity in the countries in the southern hemisphere are also relatively unique. Although SARS-CoV-2 is characterized by high activity in low temperature seasons, no direct evidence related to the characteristics of latitude distribution has been found. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved algorithm based on the original two-dimensional (2D) multifractal detrended fluctuation analysis (2D MF-DFA) that involves increasing the number of cumulative summations in the computational steps of 2D MF-DFA. The proposed method aims to modify the distribution of the generalized Hurst exponent to ensure that skin lesion image features are extracted based on enhanced multifractal features. We calculate the generalized Hurst exponent using 0, 1, or 2 cumulative summation processes. A support vector machine (SVM) is adopted to examine the classification performance under these three conditions. Computation shows that the process involving two cumulative summations achieves an accuracy, sensitivity, and specificity of 9 5. 6 9 ± 0. 1 1 7 4 % , 9 4. 2 5 ± 0. 0 9 4 2 % , and 9 7. 6 3 ± 0. 1 4 6 6 % , respectively, which indicates that its performance is much better than with 0 and 1 cumulative summations. [ABSTRACT FROM AUTHOR]

In this paper, we explore the periodicity of the multifractal phenomenon of stock price caused by public relations events using the multifractal detrended fluctuation analysis (MF-DFA) method. The Tesla stock closing price in the context of the Tesla rights defense event is used to obtain the generalized Hurst exponent and multifractal spectrum. We find that the stock price exhibits multifractal characteristics. Afterwards, we conduct a multifractal time-varying analysis using high-frequency data for 12 time periods before and after the Tesla rights defense event, which happened on 19 April 2021. We calculate the Δh and Δα before and after the rights defense event to measure the degree of multifractal of Tesla stock price. In addition, we also compute the difference between the two Δh and two Δα, which are regarded as D1 and D2. The results show that the Tesla stock market efficiency degree raised as the period time increases until the eighth period. And after the eighth period, both D1 and D2 tend to be stable, and the degree of market efficiency caused by the Tesla rights defense event remains almost unchanged. In addition, we use three broad market indices in the context of the Russia–Ukraine conflict to test the universality. The three broad market indices are Dow Jones Industrial Average (DJIA), Shanghai Stock Exchange Composite Index (SSEC) and Nikkei 225 ETF (N225). The results demonstrate that with the extending of time period, D1 and D2 gradually stabilize, indicating that public memorability will diminish and the market efficiency almost remains unchanged. [ABSTRACT FROM AUTHOR]

Accurate classification of magnetic resonance imaging (MRI) is an urgent need in clinical medicine. In this study, we explore an integrated classification model using multifractal detrended fluctuation analysis (MF-DFA) and Phase-Field models to develop a novel classifier that ensures high classification accuracy. A nonlinear hyperplane can be generated through the Phase-Field model and a dataset can be subsequently classified. First, two different types of MRI datasets are characterized in two-dimensional (2D) and three-dimensional (3D) spaces after feature extraction using Kolmogorov complexity (KC), Shannon entropy (SNE) and Higuchi's Hurst exponent (HHE). For small samples, a classification effect with 100% Accuracy, Recall and Precision can be achieved. However, for large samples, a good classification effect cannot be achieved. Therefore, we propose a novel MF-DFA-Phase-Field hybrid MRI classification method that also achieves a good classification effect on large samples. The effectiveness and robustness of the proposed MF-DFA-Phase-Field classifier will be analyzed using the generated synthetic data. Subsequently, the two datasets are represented in 2D and 3D computational spaces, where the generalized Hurst exponent computed by MF-DFA is used as the representation coordinate. For the first MRI dataset, the Accuracy, Recall, and Precision of the results for the classification metrics were 100%. In addition, we adopted another dataset with more complex image features and a larger sample size, achieving Accuracy, Recall and Precision of 92.65%, 92.85% and 92.87%, respectively. The Accuracy, Recall and Precision of the classification model based on a support vector machine (SVM) using the same dataset with 11 Hurst exponents as input vectors are 86.32%, 88.50% and 87.46% respectively. These results are all less than those of the proposed model. Similarly, our model performed better in other aspects than those by other scholars, such as MP-CNN and FC-CNN. • Multifractal detrended fluctuation analysis combined with phase-field is proposed. • The proposed system is used in MRIs classification. • The proposed system can classify MRIs datasets with high accuracy. • The classification performance is higher than SVM model and some other models. [ABSTRACT FROM AUTHOR]