On multi-transitivity with respect to a vector

A topological dynamical system $(X,f)$ is said to be multi-transitive if for every $n\in\mathbb{N}$ the system $(X^{n}, f\times f^{2}\times \dotsb\times f^{n})$ is transitive. We introduce the concept of multi-transitivity with respect to a vector and show that multi-transitivity can be characterized by the hitting time sets of open sets, answering a question proposed by Kwietniak and Oprocha [On weak mixing, minimality and weak disjointness of all iterates, Erg. Th. Dynam. Syst., 32 (2012), 1661--1672]. We also show that multi-transitive systems are Li-Yorke chaotic.Comment: 11 page

Thomson backscattering in combined two laser and magnetic field

The Thomson backscattering of an electron moving in combined fields is studied by a dynamically assisted mechanism. The combined fields are composed of two co-propagating laser fields and a magnetic field, where the first laser field is strong and low-frequency while the second is weak and high-frequency, relatively. The dependence of fundamental frequency of emission on the ratio of incident laser high-to-low frequency is presented and the spectrum of backscattering is obtained. It is found that, with a magnetic field, the peak of the spectrum and the corresponding radiation frequency are significantly larger in case of two-laser than that in case of only one laser. They are also improved obviously as the frequency of the weak laser field. Another finding is the nonlinear correlation between the emission intensity of the backscattering and the intensity of the weak laser field. These results provide a new possibility to adjust and control the spectrum by changing the ratios of frequency and intensity of the two laser fields.Comment: 13 pages, 4 figure

Effects of steroid hormones on lipid metabolism in sexual dimorphism: A Mendelian randomization study

BackgroundAlthough the role of steroid hormones in lipid levels has been partly discussed in the context of separate sexes, the causal relationship between steroid hormones and lipid metabolism according to sex has not been elucidated because of the limitations of observational studies. We assessed the relationship between steroid hormones and lipid metabolism in separate sexes using a two-sample Mendelian randomization (MR) study.MethodsInstrumental variables for dehydroepiandrosterone sulfate (DHEAS), progesterone, estradiol, and androstenedione were selected. MR analysis was performed using inverse-variance weighted, MR-Egger, weighted median, and MR pleiotropy residual sum and outlier tests. Cochran’s Q test, the MR-Egger intercept test, and leave-one-out analysis were used for sensitivity analyses.ResultsThe results showed that the three steroid hormones affected lipid metabolism and exhibited sex differences. In males, DHEAS was negatively correlated with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (P = 0.007; P = 0.006; P = 0.041, respectively), and progesterone was negatively correlated with TC and LDL-C (P = 0.019; P = 0.038, respectively). In females, DHEAS was negatively correlated with TC (P = 0.026) and androstenedione was negatively correlated with triglycerides and apolipoprotein A (P = 0.022; P = 0.009, respectively). No statistically significant association was observed between the estradiol levels and lipid metabolism in male or female participants.ConclusionsOur findings identified sex-specific causal networks between steroid hormones and lipid metabolism. Steroid hormones, including DHEAS, progesterone, and androstenedione, exhibited beneficial effects on lipid metabolism in both sexes; however, the specific lipid profiles affected by steroid hormones differed between the sexes

Galaxy Morphology Classification Using Multi-Scale Convolution Capsule Network

The classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some deficiencies in spatial feature representation and classification accuracy. In this study, we present a multi-scale convolutional capsule network (MSCCN) model for the classification of galaxy morphology. First, this model improves the convolutional layers through using a multi-branch structure to extract multi-scale hidden features of galaxy images. In order to further explore the hidden information in the features, the multi-scale features are encapsulated and fed into the capsule layer. Second, we use a sigmoid function to replace the softmax function in dynamic routing, which can enhance the robustness of MSCCN. Finally, the classification model achieving 97% accuracy, 96% precision, 98% recall, and 97% F1-score under macroscopic averaging. In addition, a more comprehensive model evaluation were accomplished in this study. We visualized the morphological features for the part of sample set, which using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. The results shows that the model has the better generalization ability and robustness, it can be effectively used in the galaxy morphological classification