Investigation on the Sex Education Status of Children in Minority’ Areas—Take Dehong Dai Jingpo Autonomous Prefecture as an Example

In order to grasp the actual situation of sex education of ethnic minority children in the border areas, this study takes children as the main object to understand the situation of sex education. 140 children aged 9-12 years in Dehong Prefecture were investigated by questionnaire. Through investigation and research, it is found that ethnic minority children have a low level of cognition of sexual knowledge, and lack of knowledge of sexual morality and sexual psychology. From the perspective of ethnic distribution, Lisu and De’ang children are most concerned about sexual related physiological knowledge, while Dai children are less concerned about it. Ethnic minority children’s access to sexual education is mainly through media, family education and learning School education comes next. And boys are more willing to learn some sexual knowledge through their friends and classmates than girls. In family life, parents talk about sexual topics with their parents and children less frequently, but most children believe that parents can give them useful help in sex, and children are more inclined to consult their parents when facing sexual questions

Three essays on asset pricing in financial market

Ph.DDOCTOR OF PHILOSOPH

Spatiotemporal patterns and determinants of renewable energy innovation: Evidence from a province-level analysis in China

China’s renewable energy innovation is essential for realizing its carbon neutrality targets and the low-carbon transition, but few studies have spatially examined its characteristics and spillover effects. To fill the research gap, this study investigates its distribution and trends from a spatiotemporal dimension and focuses on the spatial effects of the influencing factors to identify those that have a significant impact on renewable energy innovation by using China’s provincial panel data from 2006 to 2019. The results show the following findings. (1) Renewable energy innovation shows distinct spatial differences across China’s provinces such that it is high in the east and south and low in the west and north, which exhibits spatial locking and path-dependence. (2) There is a positive spatial correlation with renewable energy innovation. (3) R&D investment and GDP per capita significantly promote renewable energy innovation, but the former effect is mainly observed in the local area, whereas the latter shows spatial effects. More market-oriented policies should be taken for the improvement of renewable energy innovation and the establishment of regional coordination mechanisms are proposed

Supervised local descriptor learning for human action recognition

Local features have been widely used in computer vision tasks, e.g., human action recognition, but it tends to be an extremely challenging task to deal with large-scale local features of high dimensionality with redundant information. In this paper, we propose a novel fully supervised local descriptor learning algorithm called discriminative embedding method based on the image-to-class distance (I2CDDE) to learn compact but highly discriminative local feature descriptors for more accurate and efficient action recognition. By leveraging the advantages of the I2C distance, the proposed I2CDDE incorporates class labels to enable fully supervised learning of local feature descriptors, which achieves highly discriminative but compact local descriptors. The objective of our I2CDDE is to minimize the I2C distances from samples to their corresponding classes while maximizing the I2C distances to the other classes in the low-dimensional space. To further improve the performance, we propose incorporating a manifold regularization based on the graph Laplacian into the objective function, which can enhance the smoothness of the embedding by extracting the local intrinsic geometrical structure. The proposed I2CDDE for the first time achieves fully supervised learning of local feature descriptors. It significantly improves the performance of I2C-based methods by increasing the discriminative ability of local features while greatly reducing the computational burden by dimensionality reduction to handle large-scale data. We apply the proposed I2CDDE algorithm to human action recognition on four widely used benchmark datasets. The results have shown that I2CDDE can significantly improve I2C-based classifiers and achieves state-of-the-art performance

Novel Nonphosphorylated Peptides with Conserved Sequences Selectively Bind to Grb7 SH2 Domain with Affinity Comparable to Its Phosphorylated Ligand

The Grb7 (growth factor receptor-bound 7) protein, a member of the Grb7 protein family, is found to be highly expressed in such metastatic tumors as breast cancer, esophageal cancer, liver cancer, etc. The src-homology 2 (SH2) domain in the C-terminus is reported to be mainly involved in Grb7 signaling pathways. Using the random peptide library, we identified a series of Grb7 SH2 domain-binding nonphosphorylated peptides in the yeast two-hybrid system. These peptides have a conserved GIPT/K/N sequence at the N-terminus and G/WD/IP at the C-terminus, and the region between the N-and C-terminus contains fifteen amino acids enriched with serines, threonines and prolines. The association between the nonphosphorylated peptides and the Grb7 SH2 domain occurred in vitro and ex vivo. When competing for binding to the Grb7 SH2 domain in a complex, one synthesized nonphosphorylated ligand, containing the twenty-two amino acid-motif sequence, showed at least comparable affinity to the phosphorylated ligand of ErbB3 in vitro, and its overexpression inhibited the proliferation of SK-BR-3 cells. Such nonphosphorylated peptides may be useful for rational design of drugs targeted against cancers that express high levels of Grb7 protein

Field emission enhancement of Au-Si nano-particle-decorated silicon nanowires

Au-Si nano-particle-decorated silicon nanowire arrays have been fabricated by Au film deposition on silicon nanowire array substrates and then post-thermal annealing under hydrogen atmosphere. Field emission measurements illustrated that the turn-on fields of the non-annealed Au-coated SiNWs were 6.02 to 7.51 V/μm, higher than that of the as-grown silicon nanowires, which is about 5.01 V/μm. Meanwhile, after being annealed above 650°C, Au-Si nano-particles were synthesized on the top surface of the silicon nanowire arrays and the one-dimensional Au-Si nano-particle-decorated SiNWs had a much lower turn-on field, 1.95 V/μm. The results demonstrated that annealed composite silicon nanowire array-based electron field emitters may have great advantages over many other emitters

A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States

Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions in high sea states. A hybrid neural network is proposed that combines long short-term memory (LSTM) and convolutional neural network (CNN) in parallel. The motivation is to extract the nonlinear dynamic characteristics and the hydrodynamic memory information through the advantage of CNN and LSTM, respectively. For the feature selection, the time histories of motion states and wave heights are selected to involve sufficient information. Taken a scaled KCS as the study object, the ship motions in sea state 7 irregular long-crested waves are simulated and used for the validation. The results show that at least one period of roll motion can be accurately predicted. Compared with the single LSTM and CNN methods, the proposed method has better performance in predicting the amplitude of roll angles. Besides, the comparison results also demonstrate that selecting motion states and wave heights as feature space improves the prediction accuracy, verifying the effectiveness of the proposed method

Observation of Majorana fermions with spin selective Andreev reflection in the vortex of topological superconductor

Majorana fermion (MF) whose antiparticle is itself has been predicted in condensed matter systems. Signatures of the MFs have been reported as zero energy modes in various systems. More definitive evidences are highly desired to verify the existence of the MF. Very recently, theory has predicted MFs to induce spin selective Andreev reflection (SSAR), a novel magnetic property which can be used to detect the MFs. Here we report the first observation of the SSAR from MFs inside vortices in Bi2Te3/NbSe2 hetero-structure, in which topological superconductivity was previously established. By using spin-polarized scanning tunneling microscopy/spectroscopy (STM/STS), we show that the zero-bias peak of the tunneling differential conductance at the vortex center is substantially higher when the tip polarization and the external magnetic field are parallel than anti-parallel to each other. Such strong spin dependence of the tunneling is absent away from the vortex center, or in a conventional superconductor. The observed spin dependent tunneling effect is a direct evidence for the SSAR from MFs, fully consistent with theoretical analyses. Our work provides definitive evidences of MFs and will stimulate the MFs research on their novel physical properties, hence a step towards their statistics and application in quantum computing.Comment: 4 figures 15 page