Optimizing Guiyang's Universal Kindergarten Policy: Insights from Interview-Based Analysis

The purpose of this study is to understand the effectiveness and implementation of the universal kindergarten policy in Guiyang City, China, through the interview method and to propose strategies for improvement. The qualitative survey research project involved selecting a representative of three government workers and three kindergarten principals from six districts in Guiyang City, Southwest China, to analyze the status and challenges of the implementation of the universal kindergarten policy in Guiyang City. The study found that there is a lack of high-quality educators in the universal kindergartens in Guiyang, as well as a need to strengthen the channels of policy dissemination. Based on these findings, the researchers made recommendations, including establishing a standardized training and employment mechanism for kindergarten teachers, building an international exchange platform for universal kindergarten teachers, and upgrading the teaching force to improve the quality of education. Policy dissemination channels were also enhanced to increase parents' awareness of the policy. The study also pointed out the limitations of this study, including sample limitations and problems with interviewing techniques. The study recommended that the sample be expanded to explore a broader range of policy issues in future studies. It is also recommended that the interests of the government, kindergartens, and parents in the process of policy formulation and implementation be considered to formulate more targeted policies to promote the equitable development of pre-primary educatio

An analysis of the stability in multivariate correlation structures

Analysing the instability in the multivariate correlation structure, the present thesis starts from assessing in-sample and out-of-sample performances of multivariate GARCH models with or without a structural break. The result emphasizes the importance of correlation change point detection for model fittings. We then propose semi–parametric CUSUM tests to detect a change point in the covariance structures of non–linear multivariate models with dynamically evolving volatilities and correlations. The asymptotic distributions of the proposed statistics are derived under mild conditions. Our simulations show that, even though the nearly unit root property distorts the size and power of tests, the standardization of the data with conditional standard deviations in multivariate volatility models can correct such distortions. Lastly, concerning classical trimmed issue in change point test, we extend the semi-parametric CUSUM to weighted CUSUM tests, which enhances the power across either ends of a sample. A Monte Carlo simulation study suggests that weighted CUSUM tests exhibit better performances than unweighted ones in finite samples. Regarding empirical applications, we show the absorption ratio is a leading indicator of the financial fragility, and we study global financial contagion effect, also we investigate unexpected events in the U.S. equity market

Intraday foreign exchange rate volatility forecasting: univariate and multilevel functional GARCH models

This paper seeks to predict conditional intraday volatility in foreign exchange (FX) markets using functional Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) models. We contribute to the existing functional GARCH-type models by accounting for the stylised features of long-range and cross-dependence through estimating the models with long-range dependent and multi-level functional principal component basis functions. Remarkably, we find that taking account of cross-dependency dynamics between the major currencies significantly improves intraday conditional volatility forecasting. Additionally, incorporating intraday bid-ask spread using a functional GARCH-X model adds explainability of long-range dependence and further enhances predictability. Intraday risk management applications are presented to highlight the practical economic benefits of our proposed approaches.Comment: 43 pages, 5 figures, 8 table

The Gravitational Ringing of a Spherically Symmetric Black Hole Surrounded by Dark Matter Spike

Supermassive black holes at the center of each galaxy may be surrounded by dark matter. Such dark matter admits a spike structure and vanishes at a certain distance from the black hole. This dark matter will impact the spacetime near the black hole and the related ringing gravitational waves can show distinguished features to the black hole without dark matter. In the present work, we calculate the quasi-normal modes who dominate the ringdown process of the perturbed black holes surrounded by dark matter spike. We find that the isospectrality for black holes with dark matter spike will break down. And more the relative ringing frequency difference between the black holes with and without dark matter can be as large as $10^{-2}$. These features can be used in future gravitational wave detection about extremal mass ratio inspiral systems to probe the existence of dark matter around supermassive black holes.Comment: 14 pages, 6 figures, 5 table

THE EFFECTS OF STATIC STRETCHING AFTER STRENUOUS TRAINING ON ULTRASTRUCTURE AND FLEXIBILITY OF RATS' GASTROCNEMIUS

The purpose of the present study was to investigate effects of static stretching after strenuous training on the ultrastructure and flexibility of rats' gastrocnemius. 24 male Sprague-Dawley rats were randomly divided into three groups: normal control (NC), training control(TC) and stretching group(ST). The results were as follows: 1) The myofilaments became supercontracted and Z discs were obscure in TC. On the contrary, the myofilaments arranged orderly and the Z lines were clear and the mitochondrial cristas were manifolded in ST. 2) Compared with NC, the ultimate tensile strength of gastrocnemius was increased in TC, while the Max. deformation of gastorcnemius was decreased. However, the Max. deformation in ST was increased than that of NC. The conclusion was that the ultrastructure of muscle was resumed and the ability of distortion and flexibility was improved by static stretching, which decreased the risk of injury

Electrical transport and magnetic properties of the triangular-lattice compound Zr2_2NiP2_2

We report the first investigation of the electrical and magnetic properties of the triangular-lattice compound Zr$_2$NiP$_2$ (space group $P$6$_3$/$mmc$). The temperature evolution of electrical resistivity follows the Bloch-Gr\"uneisen-Mott law, and exhibits a typically metallic behavior. No transition is visible by both electrical and magnetic property measurements, and nearly no magnetization is detected ($M_0$ $<$ 0.002$\mu_\mathrm{B}$/Ni) down to 1.8 K up to 7 T. The metallic and nonmagnetic characters are well understood by the first-principles calculations for Zr$_2$NiP$_2$.Comment: 16 pages, 4 figure

Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition

Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter-task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis.Comment: Pattern Recognitio

Tests for conditional heteroscedasticity of functional data

Functional data objects derived from high-frequency financial data often exhibit volatility clustering. Versions of functional generalized autoregressive conditionally heteroscedastic (FGARCH) models have recently been proposed to describe such data, however so far basic diagnostic tests for these models are not available. We propose two portmanteau type tests to measure conditional heteroscedasticity in the squares of asset return curves. A complete asymptotic theory is provided for each test. We also show how such tests can be adapted and applied to model residuals to evaluate adequacy, and inform order selection, of FGARCH models. Simulation results show that both tests have good size and power to detect conditional heteroscedasticity and model mis-specification in finite samples. In an application, the tests show that intra-day asset return curves exhibit conditional heteroscedasticity. This conditional heteroscedasticity cannot be explained by the magnitude of inter-daily returns alone, but it can be adequately modeled by an FGARCH(1,1) model