Reducible Conformal Minimal Immersion with Constant Curvature from S^2 to Q_6

The geometry of conformal minimal two-spheres immersed in G(2,6;R) is studied in this paper by harmonic maps. Then in most cases, we determine the linearly full reducible conformal minimal immersions from S^2 to G(2,8;R) identified with the complex hyperquadric Q_6. We also give some examples, up to an isometry of G(2,8;R), in which none of the spheres are congruent, with the same Gaussian curvature

Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention

Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management

Investigation into the differential effects of subtitles (first language, second language, and bilingual) on second language vocabulary acquisition

Video recordings can be subtitled in three ways: with first language (L1) subtitles, with second language (L2) subtitles, or with first language plus second language (bilingual or L1+L2) subtitles. The first two types of subtitles are widely discussed in previous research with regard to how they affect language learning. However, the effects of bilingual subtitles have not been widely studied. This study aims to examine the pedagogical effects of bilingual subtitles on vocabulary acquisition in the L2 classroom. A seven-week quasi-experimental study was conducted with four English-major classes in year-3 in a Chinese university: three experimental groups and one control group. Students in the three experimental classes were exposed to three documentary films on very similar topics with the three different types of subtitles in turn. They then took a vocabulary test relating to the lexical items encountered in the films. At the end of the experiment, they were given a questionnaire to explore their opinions towards differential subtitles in relation to their language learning. The results demonstrated a significant advantage of bilingual subtitling in videos for students’ receptive vocabulary knowledge and recall at post-test and this advantage was maintained at delayed post-test. The bilingual subtitles probably are more effective than monolingual subtitles with regard to students’ vocabulary acquisition in short-term and long-term. Also, bilingual subtitles were preferred by a majority of students in respect of video understanding and vocabulary learning. L2 subtitles were favoured by more students for improving their listening comprehension. Pedagogical implications for the use of differential subtitles in the L2 classroom are discussed

Atmospheric Pollution and Microecology of Respiratory Tract

This chapter elaborates the source and ingredients of atmospheric pollutants, the microecology of respiratory tract in animals and humans and the effect of atmospheric pollution on it and thus clarifies the relationship between air pollution and microecology of the respiratory tract based on the experiments

Using simulated Tianqin gravitational wave data and electromagnetic wave data to study the coincidence problem and Hubble tension problem

In this paper, we use electromagnetic wave data (H0LiCOW, $H(z)$, SNe) and gravitational wave data (Tianqin) to constrain the interacting dark energy (IDE) model and investigate the Hubble tension problem and coincidences problem. By combining these four kinds of data (Tianqin+H0LiCOW+SNe+$H(z)$), we obtained the parameter values at the confidence interval of $1\sigma$: $\Omega_m=0.36\pm0.18$, $\omega_x=-1.29^{+0.61}_{-0.23}$, $\xi=3.15^{+0.36}_{-1.1}$, and $H_0=70.04\pm0.42$ $kms^{-1}Mpc^{-1}$. According to our results, the best valve of $H_0$ show that the Hubble tension problem can be alleviated to some extent. In addition, the $\xi+3\omega_x = -0.72^{+2.19}_{-1.19}(1\sigma)$ of which the center value indicates the coincidence problem is slightly alleviated. However, the $\xi+3\omega_x = 0$ is still within the $1\sigma$ error range which indicates the $\Lambda$CDM model is still the model which is in best agreement with the observational data at present. Finally, we compare the constraint results of electromagnetic wave and gravitational wave on the model parameters and find that the constraint effect of electromagnetic wave data on model parameters is better than that of simulated Tianqin gravitational wave data.Comment: The article has been accepted by Chinese Physics

Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research. Deep learning methods have achieved good results in medical named entity recognition (NER). However, we find that existing methods face great challenges when dealing with the nested named entities. In this work, we propose a novel method, referred to as ASAC, to solve the dilemma caused by the nested phenomenon, in which the core idea is to model the dependency between different categories of entity recognition. The proposed method contains two key modules: the adaptive shared (AS) part and the attentive conditional random field (ACRF) module. The former part automatically assigns adaptive weights across each task to achieve optimal recognition accuracy in the multi-layer network. The latter module employs the attention operation to model the dependency between different entities. In this way, our model could learn better entity representations by capturing the implicit distinctions and relationships between different categories of entities. Extensive experiments on public datasets verify the effectiveness of our method. Besides, we also perform ablation analyses to deeply understand our methods

Condition trend prediction of aero-generator based on particle swarm optimization and fuzzy integral

In order to improve and enhance the prediction accuracy and efficiency of aero-generator running trend, grasp its running condition, and avoid accidents happening, in this paper, auto-regressive and moving average model (ARMA) and least squares support vector machine (LSSVM) which are used to predict its running trend have been optimized using particle swarm optimization (PSO) based on using features found in real aero-generator life test, which lasts a long period of time on specialized test platform and collects mass data that reflects aero-generator characteristics, to build new models of PSO-ARMA and PSO-LSSVM. And we use fuzzy integral methodology to carry out decision fusion of the predicted results of these two new models. The research shows that the prediction accuracy of PSO-ARMA and PSO-LSSVM has been much improved on that of ARMA and LSSVM, and the results of decision fusion based on fuzzy integral methodology show further substantial improvement in accuracy than each particle swarm optimized model. Conclusion can be drawn that the optimized model and the decision fusion method presented in this paper are available in aero-generator condition trend prediction and have great value of engineering application

Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China

Spartina alterniflora (S. alterniflora) is one of the most harmful invasive plants in China. Google Earth (GE), as a free software, hosts high-resolution imagery for many areas of the world. To explore the use of GE imagery for monitoring S. alterniflora invasion and developing an understanding of the invasion process of S. alterniflora in the Zhangjiang Estuary, the object-oriented method and visual interpretation were applied to GE, SPOT-5, and Gaofen-1 (GF-1) images. In addition, landscape metrics of S. alterniflora patches adjacent to mangrove forests were calculated and mangrove gaps were recorded by checking whether S. alterniflora exists. The results showed that from 2003–2015, the areal extent of S. alterniflora in the Zhangjiang Estuary increased from 57.94 ha to 116.11 ha, which was mainly converted from mudflats and moved seaward significantly. Analyses of the S. alterniflora expansion patterns in the six subzones indicated that the expansion trends varied with different environmental circumstances and human activities. Land reclamation, mangrove replantation, and mudflat aquaculture caused significant losses of S. alterniflora. The number of invaded gaps increased and S. alterniflora patches adjacent to mangrove forests became much larger and more aggregated during 2003–2015 (the class area increased from 12.13 ha to 49.76 ha and the aggregation index increased from 91.15 to 94.65). We thus concluded that S. alterniflora invasion in the Zhangjiang Estuary had seriously increased and that measures should be taken considering the characteristics shown in different subzones. This study provides an example of applying GE imagery to monitor invasive plants and illustrates that this approach can aid in the development of governmental policies employed to control S. alterniflora invasion. View Full-Tex

Feminization of the health workforce in China: exploring gendered composition from 2002 to 2020

Background: Feminization of health workforce has been globally documented, but it has not been investigated in China. This study aims to analyze changes in the gendered composition of health workforce and explore the trend in different types of health workforce, health organizations and majors within China’s health system. Methods: The data were collected from China Health Statistical Yearbook from 2002 to 2020. We focused on health professionals including doctors, nurses, and pharmacists in health organizations. Trend analysis was employed to examine the change in the ratio of female health workforce over 18 years. The estimated average annual percent change (AAPC) was estimated, and the reciprocals of variances for the female ratios were used as weights. Results: In China, health professionals increased from 4.7 million in 2002 to 10.68 million in 2020. Health professionals per 1000 population increased from 3.41 in 2002 to 7.57 in 2020. The ratio of female health professionals significantly increased from 63.85% in 2002 to 72.4% in 2020 (AAPC = 1.04%, 95% CI 0.96–1.11%, P < 0.001). Female doctors and pharmacists increased 4.7 and 7.9 percentage points from 2002 to 2020. Female health workers at township health centers, village clinics, centers for disease control and prevention had higher annual increase rate (AAPC = 1.67%, 2.25% and 1.33%, respectively) than those at hospital (0.70%) and community health center (0.5%). Female doctors in traditional Chinese medicine, dentistry and public health had higher annual increase rate (AAPC = 1.82%, 1.53% and 1.91%, respectively) than female clinical doctor (0.64%). Conclusions: More women are participating in the healthcare sector in China. However, socially lower-ranked positions have been feminizing faster, which could be due to the inherent and structural gender norms restricting women’s career. More collective and comprehensive system-level actions will be needed to foster a gender-equitable environment for health workforce at all levels