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Daily average temperature data from 48 meteorological stations in Chinese oases that are within the distribution area of Populus euphraticawere analyzed to determine the spatiotemporal responses of this tree to climate change. Specifically, the start and end date as well as the number of days that comprised the growing season were analyzed with a multi-year trend line and using the Mann-Kendall mutation test, inverse distance weighted interpolation (IDW) in the software ArcGIS, a Morlet wavelet power spectrum, and correlation analysis. The results of this study show that, over the last 56 years, the start date of the P. euphraticagrowing season has advanced, while the end date has been postponed, and the number of days that comprise the growing season have gradually increased. The changing trend rates recovered in this analysis for these three time slices are –1.34 d/10 a, 1.33 d/10a, and 2.66 d/10a (α ⩾ 0.001), respectively. Data show that while spatial disparity is extremely significant, it is nevertheless the case that along a southwest-to-northeast transect of Chinese oases, the later the start date of the P. euphraticaseason, the sooner the end data and the shorter the growing season. Mutations points in start and end date, as well as for the growing season overall were observed in 2001, 1989, and 1996, respectively, and the data presented in this paper show that, in particular, the date of this end of this period is most sensitive to climate warming. Growing season cycles for P. euphraticaare between 3.56 years and 7.14 years, consistent with the periodicity of El Niño events, while a start date cycle between 3.56 years and 4.28 years is consistent with atmospheric circulation cyclicity. The causal analysis presented in this paper shows that the Asian polar vortex area index (APVAI), the Qinghai- Tibet Plateau index (TPI), the westerly circulation index (WCI), and carbon dioxide emissions (CDE) are the main factors influencing spatiotemporal changes in the growth of P. euphratica, the effect of latitude during the growing season is more significant than altitude, and the start date of the growing season is more significantly influenced by these factors than end date. In addition, data show that the start date, end date, and length of the growing season are all significantly correlated with their average corresponding monthly temperature (correlation coefficients are –0.875, 0.770, and 0.897; α⩾0.001). Thus, if the average temperature in March increases by 1°C, the start date of the growing season will advance by 2.21 days, while if the average temperature in October increases by the same margin then the seasonal end date will be delayed by 2.76 days. Similarly, if the average temperature between March and October increases by 1°C, the growing season will be extended by 7.78 days. The results of this study corroborate the fact that changes in the P. euphraticagrowing are sensitive to regional warming and are thus of considerable theoretical significance to our understanding of the responses of Chinese vegetation to climate change as well as to ecological restoration.

Superconducting MgB2nanofibers are prepared in high yield by vacuum pyrolysis of Mg(BH4)2(obtained by milling of NaBH4and MgCl2for 10 h under 5 bar H2, 90% yield) in a steel reactor at 460 °C for 1 h (95% yield).

Many previous studies have indicated that exploiting prior information about the underlying tensor data is a sound approach for completing missing or damaged entries. This prior information can be divided into three commonly used categories: low-rankness, local piecewise smoothness, and nonlocal self-similarity (NSS) priors. Although existing methods based on these priors have gained considerable attention, the majority of studies utilize only one or two of these priors, leading to inadequate extraction of structural information from tensors. To address this limitation and comprehensively depict the inherent structural information of underlying tensor data, this article develops a novel tensor completion framework that can simultaneously utilize the three abovementioned priors within a plug-and-play framework. More precisely, we adopt the tensor correlated total variation (t-CTV) norm as a robust representation for capturing the combined effects of low-rankness and local piecewise smoothness priors, eliminating the need for a trade-off parameter in the process; meanwhile, we introduce an advanced denoiser to explore the NSS prior. Furthermore, to address the presented optimization model, we design an alternating direction method of multipliers (ADMM) algorithm and innovatively provide its corresponding global convergence guarantees. Extensive numerical experiments on real tensor data, including color, medical and hyperspectral images, demonstrate that the proposed method surpasses various advanced approaches in terms of both quality metrics and visual effects. [ABSTRACT FROM AUTHOR]