Can the Elite Stream Improve the Academic Achievement of Senior Secondary School Students? A Study of High School Students from X City in western China based on Regression Discontinuity Analysis

The fairness and efficacy of the elite stream in general senior secondary education have long been a contentious issue. Based on the longitudinal data of the students who were enrolled in five senior secondary schools in X City in western China in 2017 and 2018, this study examined the effects of elite class streaming in improving student academic performance, using regression discontinuity analysis. The research findings showed that: despite the significant differences in the grade-10 streaming examination results and the grade-12 academic achievement between the elite class and regular class students, the gap was not markedly widened after three years of senior secondary education; the elite stream did not exhibit distinct promotive effects on the advancement of students’ overall performance when the cutoff point of elite class admission was utilized as the exogenous variable to evaluate the effects of the elite stream on student performance; both the parameter estimation and non-parametric estimation results demonstrated that the elite stream had no marked effects in improving student performance and there was no gender difference or urban vs. rural difference in the impact of the elite stream on student academic achievement

PENGARUH MANAJEMEN STRES TERHADAP KESEHATAN PSIKOLOGIS DAN LITERASI PSIKOLOGIS MAHASISWA PADA MASA PANDEMI COVID-19

Penelitian ini bertujuan untuk mengetahui pengaruh manajemen stress terhadap kesehatan psikologis dan literasi psikologis mahasiswa pada masa pandemi covid-19 di Fakultas Bahasa Asing Universitas Normal Qujing. Jenis penelitian yang digunakan dalam penelitian ini adalah penelitian kuantitatif. Sampel yang digunakan dalam penelitian ini adalah 30 sampel dengan pengumpulan data menyebarkan kuesioner. Hasil dari pengujian tingkat signifikasi diketahui bahwa variabel X berpengaruh terhadap Y1. Serta pengujian hasil tingkat signifikasi diketahui bahwa variabel X berpengaruh terhadap Y2. Sehingga hipotesis diterima yaitu terdapat pengaruh manajement stress terhadap kesehatan psikologis dan literasi psikologis. Berdasarkan hasil penelitian dan pembahasan pada penelitian ini, maka dapat disimpulkan bahwa manajemen stress berpengaruh positif dan signifikan terhadap kesehatan psikologis mahasiswa pada masa pandemi covid-19 di Fakultas Bahasa Asing Universitas Normal Qujing. . Kata Kunci: kesehatan psikologis, literasi psikologis, manajemen stress This study aims to determine the effect of stress management on psychological health and psychological literacy of students during the Covid-19 pandemic at the Faculty of Foreign Languages, Qujing Normal University. The type of research used in this research is quantitative research. The sample used in this study was 30 samples by collecting data by distributing questionnaires. The results of the significance level test show so that it can be said that variable X has an effect on Y1. As well as testing the results of the significance level so that it can be said that the X variable affects Y2. So the hypothesis is accepted that there is an effect of stress management on psychological health and psychological literacy. Based on the results of the research and discussion in this study, it can be concluded that stress management has a positive and significant effect on the psychological health of students during the Covid-19 pandemic at the Faculty of Foreign Languages, Qujing Normal University. . Keywords: psychological health, psychological literacy, stress managemen

Scalar Quantization as Sparse Least Square Optimization

Quantization can be used to form new vectors/matrices with shared values close to the original. In recent years, the popularity of scalar quantization for value-sharing applications has been soaring as it has been found huge utilities in reducing the complexity of neural networks. Existing clustering-based quantization techniques, while being well-developed, have multiple drawbacks including the dependency of the random seed, empty or out-of-the-range clusters, and high time complexity for a large number of clusters. To overcome these problems, in this paper, the problem of scalar quantization is examined from a new perspective, namely sparse least square optimization. Specifically, inspired by the property of sparse least square regression, several quantization algorithms based on $l_1$ least square are proposed. In addition, similar schemes with $l_1 + l_2$ and $l_0$ regularization are proposed. Furthermore, to compute quantization results with a given amount of values/clusters, this paper designed an iterative method and a clustering-based method, and both of them are built on sparse least square. The paper shows that the latter method is mathematically equivalent to an improved version of k-means clustering-based quantization algorithm, although the two algorithms originated from different intuitions. The algorithms proposed were tested with three types of data and their computational performances, including information loss, time consumption, and the distribution of the values of the sparse vectors, were compared and analyzed. The paper offers a new perspective to probe the area of quantization, and the algorithms proposed can outperform existing methods especially under some bit-width reduction scenarios, when the required post-quantization resolution (number of values) is not significantly lower than the original number

Development of chemical sensors for monitoring environmental emissions

Peer reviewed: YesNRC publication: Ye

Pulsed laser deposition, characterization and thermochemical stability of SrFeyCo1-yOx thin films

Peer reviewed: YesNRC publication: Ye

Establishment of a predictive nomogram for differentiated thyroid cancer: an inpatient-based retrospective study

Introduction: Differentiated thyroid cancer (DTC) is the most common malignant tumour of the endocrine system. The aim of this study was to establish a nomogram for simply and effectively predicting DTC. Material and methods: 464 inpatients who underwent thyroid nodule surgery were retrospectively analysed. Univariate logistic regression and multivariate logistic regression were used to analyse the risk factors of DTC. A nomogram was constructed for predicting DTC. Results: In this study, multivariate logistic regression found that female sex, age < 55 years, solid composition, hypoechogenicity, irregular margin, microcalcification, taller-than-wide, and cervical lymphadenopathy were independent risk factors for DTC. The area the curve (AUC) of the nomogram model indicated an excellent predictive performance of 0.920 [95% confidence interval (CI): 0.888–0.952]. The best threshold for predicting DTC was 52.4%, with sensitivity and specificity of 91.9% and 81.0%, respectively. Conclusions: we provided a simple, noninvasive, and accurate model for clinicians to predict DTC

Detection of carbon monoxide and hydrogen with non-stoichiometric perovskites, and variation in transduction modes

Peer reviewed: YesNRC publication: Ye

TEM characterization of phase separation and transformation at the thin film intefaces in the SrFeO2.5+x/SiO2/Si system

Peer reviewed: YesNRC publication: Ye