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Intrinsic defects can have an effect on the microstructure of semiconductor materials, which can change the electronic structure of the material and ultimately alter its optical and magnetic properties. In this study, first-principles calculations are performed to demonstrate that the defect formation energies of VTe and ZnCd + VTe are lower under Cd-rich conditions. Conversely, under Te-rich conditions, with the exception of Tei, the defect formation energies for TeCd, VCd, ZnCd+ TeCd, and ZnCd+ VCd are lower, indicating that these defects exhibit greater stability within the system. Furthermore, it is demonstrated that VCd, CdTe, and TeCd introduce magnetic moments into the CdTe system, with VCd contributing the largest magnetic moment. Additionally, composite defects formed by ZnCd and VCd, CdTe, and TeCd also induce spin polarization and introduce magnetic moments. However, the synergistic effect of ZnCd on CdTe defects is significant, reducing the magnetic moment introduced by the CdTe defects. Moreover, ZnCd + VTe composite defects, VTe + VCd composite defects, and VTe, Cdi, and Tei do not introduce magnetic moments into the system. For the CdTe and ZnCd + CdTe defect systems, there is a notable impact on the optical properties, such as the refractive index and reflectance, compared to the perfect CdTe system. When CdTe defects are present, defect energy levels are introduced within the bandgap, affecting electronic transitions between energy bands, which, in turn, influences the magnetic and optical properties of the system. Additionally, it is found that the optical properties of CdTe systems containing intrinsic or composite defects are isotropic, exhibiting weak absorption in the visible light range. However, VTe and TeCd defects can enhance the absorption of visible light to some extent. [ABSTRACT FROM AUTHOR]

In the last decade, the spatio-temporal patterns of PM2.5 on various scales, ranging from global, continent, and country to regional levels, has been the focus of considerable studies. However, these studies on spatio-temporal variability have concentrated primarily on changes in the spatial distribution patterns of regional PM2.5 concentrations and ignored temporal characteristics at a local site from a heterogeneous surface, such as local variability, persistence, and stability of PM2.5 exposure. Understanding the temporal characteristics of PM2.5 concentration changes at the local scale will help determine the local impacts of PM2.5, such as local exposure risk and vulnerability to PM2.5. This study aims to reveal the local characteristics of temporal variation at the scale of a prefecture-level city and its distinct-varying patterns from those at the provincial scale by using the annual satellite-derived PM2.5 concentration product from 2000 to 2015. The evolutionary trends, stability, and persistence of annual changes were discovered with a set of time series analysis methods, such as linear regression analysis + F-test, coefficient of variation method, and Hurst index. This study uses PM2.5 product data for a total of 16 years, from 2000 to 2015, and uses time series analysis methods, such as Theil–Sen median trend analysis + Mann–Kendall test, one-dimensional linear regression analysis + F-test, coefficient of variation method, and Hurst index, to reveal the temporal variation characteristics and spatial patterns of PM2.5 in Jiangsu Province. The results show that the increasing trends or slopes of annual averaged PM2.5 concentrations in Jiangsu Province are not consistent at the prefecture-level city scale, but they are consistent in northern, central and southern Jiangsu at a larger upward trend since 2000. The areas with significant increasing trends are concentrated in Xuzhou and Lianyungang and other northern cities. From the viewpoint of variability, the areas in medium and high variability are mainly aggregated in the areas north of the Yangtze River. According to the combination of persistence analysis and trend analysis, future variation in PM2.5 concentrations indicates an inverse persistence for an increasing trend, meaning the air quality decline in Jiangsu will slow. [ABSTRACT FROM AUTHOR]

Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 across Mainland China during 1998-2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5 concentrations greater than 35 μg/m3 significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM2.5. Additionally, the Moran's I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM2.5 in Mainland China. The effects of each latent factor on PM2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM2.5 should be formulated in terms of the local impacts of specific factors. [ABSTRACT FROM AUTHOR]

Due to the limitation of labeled training samples, computational complexity, and other difficulties, active learning (AL) algorithms aiming at finding the most informative training samples have been an active topic of research in remote sensing image classification in the last few years. Usually, AL follows an iterative scheme, and the search of new samples relies on the whole image, resulting in that an approach may turn out to be prohibitive when the data sets are huge, e.g., hyperspectral data. Large amounts of unlabeled samples are easy to collect indeed, with respect to the cost of labeled sample collection. However, algorithm complexity, data storage capacity and processing times are also limited. Therefore, a sample set smaller in size, and consisting of the most valuable information, is preferable. In this work, we propose a design protocol to generate a more significant candidate sample set for active learning, aiming at reducing the unlabeled sample search complexity, and eventually improving the classification performance. The basic idea is providing the initial labeled and unlabeled samples that are composed of mixed or pure samples for AL heuristics, to find out which one is better for AL from the low-cost sample design point of view. For comparison and validation purposes, six state-of-the-art AL methods (including breaking ties, margin sampling, margin sampling by closest support vectors, normalized entropy query-by-committee, multi-class level uncertainty and multi view adaptive maximum disagreement based active learning) were tested on real hyperspectral images with different resolution both with and without the proposed sample design protocol. Experimental results confirmed the advantages of the proposed technique. [ABSTRACT FROM AUTHOR]

Accurate and efficiently updated information on color-coated steel sheet (CCSS) roof materials in urban areas is of great significance for understanding the potential impact, challenges, and issues of these materials on urban sustainable development, human health, and the environment. Thanks to the development of Earth observation technologies, remote sensing (RS) provides abundant data to identify and map CCSS materials with different colors in urban areas. However, existing studies are still quite challenging with regards to the data collection and processing costs, particularly in wide geographical areas. Combining free access high-resolution RS data and a cloud computing platform, i.e., Sentinel-2A/B data sets and Google Earth Engine (GEE), this study aims at CCSS material identification and mapping. Specifically, six novel spectral indexes that use Sentinel-2A/B MSIL2A data are proposed for blue and red CCSS material identification, namely the normalized difference blue building index (NDBBI), the normalized difference red building index NDRBI, the enhanced blue building index (EBBI), the enhanced red building index (ERBI), the logical blue building index (LBBI) and the logical red building index (LRBI). These indexes are qualitatively and quantitatively evaluated on a very large number of urban sites all over the P.R. China and compared with the state-of-the-art redness and blueness indexes (RI and BI, respectively). The results demonstrate that the proposed indexes, specifically the LRBI and LBBI, are highly effective in visual evaluation, clearly detecting and discriminating blue and red CCSS covers from other urban materials. Results show that urban areas from the northern parts of P.R. China have larger proportions of blue and red CCSS materials, and areas of blue and red CCSS material buildings are positively correlated with population and urban size at the provincial level across China. [ABSTRACT FROM AUTHOR]