Characterization of the Interface Roughness of Coatings Based on Ultrasonic Reflection Coefficient Amplitude Spectrum

In order to nondestructively characterize the interface roughness of coatings effectively, the ultrasonic reflection coefficient amplitude spectrum (URCAS) involving interface roughness was derived based on the phase screen approximation theory [1]. The interface roughness was determined by a two-parameter inversion combined with a cross-correlation algorithm. For homogeneous coatings, the effects of ultrasonic wavelength λ, beam coverage, and shape variations of the coating on the roughness measurements were analyzed through numerical calculation. A series of simulations indicated that measurement errors were less than 10% when the relationship between interface roughness and wavelength satisfied Rq=1.5%λ~12%λ approximately. For inhomogeneous coatings, the attenuation coefficient shows a non-negligible effect on the URCAS. A new URCAS suited for roughness measurement of inhomogeneous coatings was formulated by considering the relationship of attenuation coefficient α(f) on frequency f, which was determined by simulations. Ultrasonic experiments were carried out on standard roughness specimens and tungsten carbide (WC) coating specimen utilizing delay line transducers. The standard roughness specimens were shown in Fig 1, whose roughness Rq were 8.5μm, 14.2μm, and 28.6μm measured by confocal laser scanning microscope (CLSM), respectively. The WC coating was sprayed on stainless steel by high velocity oxygen fuel (HOVF). Experimental results show that the roughness of standard roughness specimens obtained by the proposed ultrasonic measurement are in good agreement with the LCM observations, and the relative errors are less than 8.5%. For inhomogeneous WC coatings, the absolute error of roughness measurement is less than 2.5μm and the relative error is less than 20% between ultrasonic and metallographic methods

Bias-Flip Technique for Frequency Tuning of Piezo-Electric Energy Harvesting Devices

Devices that harvest electrical energy from mechanical vibrations have the problem that the frequency of the source vibration is often not matched to the resonant frequency of the energy harvesting device. Manufacturing tolerances make it difficult to match the Energy Harvesting Device (EHD) resonant frequency to the source vibration frequency, and the source vibration frequency may vary with time. Previous work has recognized that it is possible to tune the resonant frequency of an EHD using a tunable, reactive impedance at the output of the device. The present paper develops the theory of electrical tuning, and proposes the Bias-Flip (BF) technique, to implement this tunable, reactive impedance

Variations in Stable Carbon Isotope Composition and Leaf Traits of Picea schrenkiana

To understand the morphological and physiological responses of leaves to changes in altitudinal gradients, we examined ten morphological and physiological characteristics in one-year-old needles of Picea schrenkiana var. tianschanica at ten points along an altitudinal gradient from 1420 to 2300 m a.s.l. on the northern slopes of the Tianshan Mountains in northwest China. Our results indicated that LA, SD, LPC, and LKC increased linearly with increasing elevation, whereas leaf δ13C, LNC, Chla + b, LDMC, LMA, and Narea varied nonlinearly with changes in altitude. With elevation below 2100 m, LNC, Narea, and Chla + b increased, while LDMC and LMA decreased with increasing altitude. When altitude was above 2100 m, these properties showed the opposite patterns. Leaf δ13C was positively correlated with Narea and LNC and negatively correlated with SD and LA, suggesting that leaf δ13C was indirectly controlled by physiological and morphological adjustments along altitudinal gradients. Based on the observed maximum values in LNC, Narea, Chla + b, and LA and the minimum values in LMA and LDMC at the elevation of 2100 m, suggesting higher photosynthetic capacity and greater potential for fast growth under superior optimum zone, we concluded that the best growing elevation for P. schrenkiana var. tianschanica in the Tianshan Mountains was approximately 2100 m

Plant beta-turnover rather than nestedness shapes overall taxonomic and phylogenetic beta-diversity triggered by favorable spatial–environmental conditions in large-scale Chinese grasslands

IntroductionAlthough it is widely acknowledged that biodiversity maintains plant community assembly processes, exploring the patterns and drivers of beta-diversity (β-diversity; species variation among local plant communities) has received much less attention compared to alpha-diversity (α-diversity; species variation within a local plant community). Here, we aim to examine the patterns and spatial–environmental drivers of taxonomic and phylogenetic β-diversity, and their components such as species turnover and nestedness, in large-scale Leymus chinensis grassland communities.MethodsWe collected plant community data from 166 sites across widely distributed L. chinensis communities in northern China, and then calculated the taxonomic and phylogenetic β-diversity indices (overall, turnover and nestedness) using a pairwise dissimilarity approach. To assess the effects and to explain the variation in the patterns of β-diversity, we collected data on geospatial, climate and soil conditions. We applied descriptive statistics, Mental correlations, and multiple linear regression models to assess the patterns and spatial–environmental drivers of β-diversity.ResultsThe β-turnover, as compared to β-nestedness, exhibited a predominant influence, constituting 92.6% of the taxonomic β-diversity and 80.4% of the phylogenetic β-diversity. Most of the spatial–environmental variables were significantly positively correlated with the overall taxonomic and phylogenetic β-diversity and β-turnover, but not with β-nestedness. Climatic factors such as MAP and MAT were the strongest predictors of both taxonomic and phylogenetic β-diversity and β-turnover. The variance partitioning analysis showed that the combined effects of spatial and environmental factors accounted for 19% and 16% of the variation in the taxonomic and phylogenetic β-diversity (overall), 17% and 12% of the variation in the β-turnover, and 7% and 1% of the variation in the β-nestedness, respectively, which were higher than independent effects of either spatial or environmental factors.DiscussionAt larger spatial scales, the turnover component of β-diversity may be associated with the species complementarity effect, but dominant or functionally important species can vary among communities due to the species selection effect. By incorporating β-diversity into grassland management strategies, we can enhance the provision of vital ecosystem services that bolster human welfare, serving as a resilient barrier against the adverse effects of climate change at regional and global scales