With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more significantly to recent price changes than the simple moving average (SMA). Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. The purpose of this study is to develop an effective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD. Furthermore, the validity of the MACD-HVIX index is tested by using the trend recognition accuracy. We compare the accuracy between a MACD histogram and a MACD-HVIX histogram and find that the accuracy of using MACD-HVIX histogram is 55.55% higher than that of the MACD histogram when we use the buy-and-sell strategy. When we use the buy-and-hold strategy for 5 and 10 days, the prediction accuracy of MACD-HVIX is 33.33% and 12% higher than that of the traditional MACD strategy, respectively. We found that the new indicator is more stable. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market.

Background: A new coronavirus disease 2019 (COVID-19) has escalated to a pandemic since its first outbreak in Wuhan, China. A small proportion of patients may have difficulty in generating IgM or IgG antibodies against SARS-CoV-2, and little attention has been paid to them. Case presentations: We present two cases of confirmed COVID-19 patients and characterize their initial symptoms, chest CT results, medication, and laboratory test results in detail (including RT-PCR, IgM/ IgG, cytokine and blood cell counts). Conclusion: Both of patients with confirmed COVID-19 pneumonia failed to produce either IgM or IgG even 40 to 50 days after their symptoms onset. This work provides evidence demonstrating that at least a small proportion of patients may have difficulty in rapidly gaining immunity against SARS-CoV-2.

This paper proposes a multifractal least squares support vector machine detrended fluctuation analysis (MF-LSSVM-DFA) model. The system is an extension of the traditional MF-DFA model. To address potential overfitting or underfitting caused by the fixed-order polynomial fitting in MF-DFA, LSSVM is employed as a superior alternative for fitting. This approach enhances model accuracy and adaptability, ensuring more reliable analysis results. We utilize the p model to construct a multiplicative cascade time series to evaluate the performance of MF-LSSVM-DFA, MF-DFA, and two other models that improve upon MF-DFA from recent studies. The results demonstrate that our proposed modified model yields generalized Hurst exponents $h\left(q\right)$ and scaling exponents $\tau \left(q\right)$ that align more closely with the analytical solutions, indicating superior correction effectiveness. In addition, we explore the sensitivity of MF-LSSVM-DFA to the overlapping window size s. We find that the sensitivity of our proposed model is less than that of MF-DFA. We find that when s exceeds the limited range of the traditional MF-DFA, $h\left(q\right)$ and $\tau \left(q\right)$ are closer than those obtained in MF-DFA when s is in a limited range. Meanwhile, we analyze the performances of the fitting of the two models and the results imply that MF-LSSVM-DFA achieves a better outstanding performance. In addition, we put the proposed MF-LSSVM-DFA into practice for applications in the medical field, and we found that MF-LSSVM-DFA improves the accuracy of ECG signal classification and the stability and robustness of the algorithm compared with MF-DFA. Finally, numerous image segmentation experiments are adopted to verify the effectiveness and robustness of our proposed method.

In order to protect sensitive information from unauthorized access and illegal copy during network transmission, storage and processing, we propose a new four-dimensional hyperchaotic system (4DHS) and apply it to encryption algorithm. Firstly, the dynamical properties of 4DHS are analyzed according to the structure, and the chaotic properties are verified by dissipation, equilibrium point and lyapunov exponent. Secondly, the chaotic sequence combined with Arnold scrambling method is adopted to scramble the pixel values of the plaintext image, and the scrambled pixel matrix is diffused into the ciphertext image matrix by XOR operation. Finally, we conduct the experiments to validate the effectiveness of the proposed encryption algorithm and achieve satisfactory results. At the same time, we compare the proposed encryption algorithm with other encryption algorithms, and the excellent encryption effect of our encryption algorithm can be proved.