The hidden sexual minorities: machine learning approaches to estimate the sexual minority orientation among Beijing college students

Based on the fourth-wave Beijing College Students Panel Survey (BCSPS), this study aims to provide accurate estimation of the percentage of the potential sexual minorities among the Beijing college students by using machine learning methods. Specifically, we employ random forest (RF), an ensemble learning approach for classification and regression, to predict the sexual orientation of those who were not willing to disclose his/her inherent sexual identity. To overcome the imbalance problem arising from far different numerical proportion of sexual minority and majority members, we adopt the repeated random sub-sampling for training set by partitioning those who expressed heterosexual orientation into different number of splits and further combining each split with those who expressed sexual minority orientation. The prediction from 24-split random forest suggests that youths in Beijing with sexual minority orientation amount to 5.71%, almost two times that of the original estimation 3.03%. The results are robust to alternative learning methods and covariate sets. Besides, it is also suggested that random forest outperforms other learning algorithms, including AdaBoost, Naive Bayes, support vector machine (SVM), and logistic regression, in dealing with missing data, by showing higher accuracy, F1 score, and area under curve (AUC) value

Gradient estimate for solutions of the equation Δpv+avq=0\Delta_p v+av^{q}=0 on a complete Riemannian manifold

In this paper, we use Nash-Moser iteration method to study the local and global behaviours of positive solutions to the nonlinear elliptic equation $\Delta_pv +av^{q}=0$ defined on a complete Riemannian manifolds $(M,g)$ where $p>1$, $a$ and $q$ are constants. Under some assumptions on $a$, $p$ and $q$, we derive gradient estimates and Liouville type theorems for such positive solutions.Comment: some correction

Experimental study on failure characteristics of jointed rock mass under staged unloading conditions

Strength and deformability characteristics of rock with pre-existing fissure are governed by cracking behavior. To further research the effects of pre-existing fissures on the mechanical properties and crack coalescence process under staged unloading conditions, a series of uniaxial compression tests were carried out. In this study, cement, fine sand, and water were used to fabricate a kind of brittle rock-like material rectangular model specimen. The mechanical properties of rock-like material specimen used in this research were all in good agreement with the brittle rock materials. The pre-existing fissure was created by mica sheets and the unloading zone was created by pre-filling Polylactic acid (PLA) resin material during the molding processes the model specimen. And the staged unloading test was conducted based on polylactic acid’s hot-melt characteristics. In order to investigate the crack initiation, propagation and coalescence process, inclination angle of pre-existing fissure was set to change from 0° to 90°. The mechanism of crack propagation and coalescence under photographic monitoring was discussed. And these experimental results indicate that the failure modes in the zone above unloading zone mainly consist of four modes: separation failure, shear-slip failure, mixed shear-separation failure and compression-shear failure. The experimental results are also expected to increase the understanding of the strength failure behavior and the cracking mechanism of rock under staged unloading

Magnesium sulphate attenuate remifentanil-induced postoperative hyperalgesia via regulating tyrosine phosphorylation of the NR2B subunit of the NMDA receptor in the spinal cord

Data of PWTL and PWMT values in the study. (XLS 19 kb

Literary destination familiarity and inbound tourism: evidence from mainland China

Destination familiarity is an important non-economic determinant of tourists’ destination choice that has not been adequately studied. This study posits a literary dimension to the concept of destination familiarity —that is, the extent to which tourists have gained familiarity with a given destination through literature—and seeks to investigate the impact of this form of familiarity on inbound tourism to Mainland China. Employing the English fiction dataset of the Google Books corpus, the New York Times annotated corpus, and the Time magazine corpus, we construct two types of destination familiarity based on literary texts: affection-based destination familiarity and knowledge-based destination familiarity. The results from dynamic panel estimation (1994–2004) demonstrate that the higher the degree of affection-based destination familiarity with a province in the previous year, the larger the number of inbound tourists the following year. Examining the influence of literature and its consumption on tourism activities sheds light on the dynamics of sustainable tourism development in emerging markets