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Herein, the spatial evolution characteristics of high-level Grade A tourist attractions in the Yangtze River Delta (YRD) urban agglomeration, from 2001 to 2021, are studied by comprehensively applying the nearest neighbor index, kernel density analysis, standard deviation ellipse, and spatial autocorrelation. High-level Grade A tourist attractions are investigated using the random forest model as the driving mechanism of the spatial pattern. Results show that 1) the spatial distribution of high-level Class A tourist attractions in the YRD city cluster has grown to be an agglomeration, and the high-density areas have evolved from "point-like dispersion to regiment-like combination," gradually forming a B-shaped core density structure. 2) The spatial distribution comprises an overall "northwest–southeast" direction, a small counterclockwise rotation, the distribution of the center of gravity to the southwest migration, and the center of gravity from the territory of Suzhou City to the territory of Huzhou City. 3) The high-level Class A tourist attractions in the YRD city cluster as a whole show a strong positive spatial correlation, and the significantly clustered areas include three types: high-high (H-H), low-low (L-L), and low-high (L-H). 4) The spatial distribution of high, A-level tourist attractions in the YRD city cluster results from the combined action of the natural environment, resource endowment, socioeconomy, and policy background. Each element has a nonlinear and complex influence on the distribution of scenic spots. [ABSTRACT FROM AUTHOR]

In this article we consider the two-way ANOVA model without interaction under heteroscedasticity. For the problem of testing equal effects of factors, we propose a parametric bootstrap (PB) approach and compare it with existing the generalizedF(GF) test. The Type I error rates and powers of the tests are evaluated using Monte Carlo simulation. Our studies show that the PB test performs better than the GF test. The PB test performs very satisfactorily even for small samples while the GF test exhibits poor Type I error properties when the number of factorial combinations or treatments goes up. It is also noted that the same tests can be used to test the significance of random effect variance component in a two-way mixed-effects model under unequal error variances. [ABSTRACT FROM AUTHOR]

Abstract: This work described the size-controllable preparation of novel graphitic carbon nitride nanosheets using the interlayer space of smectite clays as a confined nanoreactor through cyanamide condensation. The prepared solids were characterized by X-ray diffraction, FT-IR, SEM and TEM. Results revealed that when ammonium-saponite was used, graphitic carbon nitride with an average crystal size of about 4.8nm, the particle size of 50–100nm and a C/N=1.931 was obtained. When montmorillonite was used, graphitic carbon nitride with a crystal size of approximate 6.0nm, the particle size of 100–150nm and a C/N=0.959 was yielded. The space-confined preparation of carbon nitride using layered clay minerals not only changes the texture of the resultant carbon nitride, but also alters the composition of products owing to the change of the whole reaction scheme to yield some new species. Such controllability over the size can be ascribed to the difference of the interlayer space and the cation exchange capacity of saponite and montmorillonite. [ABSTRACT FROM AUTHOR]