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Calcium‐ion batteries (CIBs) are considered as potential next‐generation energy storage systems due to their abundant reserves and relatively low cost. However, irreversible structural changes and weak conductivity still hinder in current CIBs cathode materials. Herein, an organic molecular intercalation strategy is proposed, in which V2O5 regulated with quinoline, pyridine, and water molecules are studied as cathode material to provide fast ion diffusion channels, large storage host, and high conductivity for Ca ions. Among them, V2O5‐quinoline (QVO) owns the largest interplanar spacing of 1.25 nm and the V‐O chains are connected with organic molecular by hydrogen bond, which stabilizes the crystal structure. As a result, QVO exhibits a specific capacity of 168 mAh g−1 at 1 A g−1 and capacity retention of 80% after 500 cycles at 5 A g−1 than the other materials. Furthermore, X‐Ray diffraction and X‐ray absorption spectroscopy results reveal a reversible order‐disorder transformation mechanism of Ca2+ for QVO, which can make full use of the abundant active sites for high capacity and simultaneously achieve fast reaction kinetics for excellent rate performance. These results demonstrate that QVO is a promising cathode material for CIBs, providing more choices for the development of high‐performance CIBs. [ABSTRACT FROM AUTHOR]

Spontaneous abortion is the most common complication in early pregnancy, the exact etiology of most cases cannot be determined. Emerging studies suggest that mutations in ciliary genes may be associated with progression of pregnancy loss. However, the involvement of primary cilia on spontaneous abortion and the underlying molecular mechanisms remains poorly understood. We observed the number and length of primary cilia were significantly decreased in decidua of spontaneous abortion in human and lipopolysaccharide (LPS)‐induced abortion mice model, accompanied with increased expression of proinflammatory cytokines interleukin (IL)−1β, IL‐6, and tumor necrosis factor (TNF)‐α. The length of primary cilia in human endometrial stromal cell (hESC) was significantly shortened after TNF‐α treatment. Knocking down intraflagellar transport 88 (IFT88), involved in cilia formation and maintenance, promoted the expression of TNF‐α. There was a reverse regulatory relationship between cilia shortening and TNF‐α expression. Further research found that shortened cilia impair decidualization in hESC through transforming growth factor (TGF)‐β/SMAD2/3 signaling. Primary cilia were impaired in decidua tissue of spontaneous abortion, which might be mainly caused by inflammatory injury. Primary cilia abnormalities resulted in dysregulation of TGF‐β/SMAD2/3 signaling transduction and decidualization impairment, which led to spontaneous abortion. [ABSTRACT FROM AUTHOR]

Vanadate materials are feasible cathodes for metal–ion batteries due to their stable layered structure, abundant valence states, and high capacity. However, much uncertainty still exists about precisely modulating intercalants to facilitate ion storage. Here, V2O5 pre‐intercalated with various transition metal ions M2+ (M═Ni, Co, Mn) are developed as model materials to analyze the coupling effect between guest ions and host material. Through density functional theory simulations, it is found that M2+ interacts with V–O chain via M 3d‐O 2p covalent bonds, and extended X‐ray absorption fine structure reveals the Ni─O interatomic distance at 1.56 Å shorter than Co─O (1.60 Å) and Mn─O (1.72 Å), suggesting the M–O band type with different covalency degree can optimize VOx polyhedron and local electronic structure. Furthermore, NiVO cathode materials with the smallest layer spacing shows higher redox voltage and better rate/cycling performance for Ca2+ storage than CoVO/MnVO, elucidating that Ni has stronger tendency to attract electrons and bonds with V–O layer tightly, thus supplying a reliable ion diffusion channel for Ca2+. Through ions pre‐intercalated techniques, this work highlights both layer spacing and physicochemical properties of intercalants affect electrochemical process, which is significant for developing high‐performance vanadate cathode materials. [ABSTRACT FROM AUTHOR]

Reliable tree canopy cover (TCC) products are vital for national forest inventory, land process modeling and forest dynamic monitoring. The new generation of space-based laser altimeter, GEDI, offers a three-dimensional (3D) insight on the forest structure, shaping the paradigm of structural variable estimation. However, the generality of newly released GEDI level-2 TCC product version 2 was less investigated across various forest types. Additionally, satellite-derived product validation usually suffers from the geolocation mismatch between satellite and reference data. In this study, we comprehensively validated the GEDI TCC product across seven forest types using the reference TCC derived from several public and private aerial LiDAR datasets after geographical registration, and crossly compared with a commonly-used passive satellite product (i.e., GFCC TCC). As the reference aerial TCC maps were derived using various aerial LiDAR instruments, we investigated the consistency of TCC estimation among them using simulation datasets and found that the distributions of TCC relative bias (biasR, %) were almost identical and the differences of relative RMSE (rRMSE, %) was less than 0.2%. Through the registration process, we found that the geolocation offsets of GEDI footprints tended to be independent of azimuth directions and their average was about 10 m, verifying the necessity of registration during the validation process. Importantly, the post-registration validation of GEDI TCC showed an average RMSE of 0.10 and an average R2 of 0.85 for all forest types, resulting in a decrease of RMSE of up to 0.15 and an increase of R2 of up to 0.33 compared to the pre-registration validation. The inter-comparison also exhibited improved consistency between GEDI and GFCC TCC products after registration. Further, we found a non-negligible dependence of GEDI TCC on the slope factor but almost independence on forest type, encouraging the spread of GEDI TCC product. [ABSTRACT FROM AUTHOR]

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for ground- and aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloud-based methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and non-photosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists. [ABSTRACT FROM AUTHOR]

Nonlinear wavelet-based estimators for spectral densities of non-Gaussian linear processes are considered. The convergence rates of mean integrated squared error (MISE) for those estimators over a large range of Besov function classes are derived, and it is shown that those rates are identical to minimax lower bounds in standard nonparametric regression model within a logarithmic term. Thus, those rates could be considered as nearly optimal. Therefore, the resulting wavelet-based estimators outperform traditional linear methods if the degree of smoothness of spectral densities varies considerably over the interval of interest, such as sharp spike, cusp, bump, etc., since linear estimators are not able to attain these rates. Unlike in classical nonparametric regression with Gaussian noise errors where thresholds are determined by normal distribution, we determine the thresholds based on a Bartlett type approximation of a quadratic form with dependent variables by its corresponding quadratic form with independent identically distributed (i.i.d.) random variables and Hanson-Wright inequality for quadratic forms in sub-gaussian random variables. The theory is illustrated with some numerical examples, and our simulation studies show that our proposed estimators are comparable to the current ones. [ABSTRACT FROM AUTHOR]

Burn severity assessment is critical for understanding the pattern of post-fire vegetation recovery and ecosystem resilience. Previous studies proposed various field criteria (e.g., Composite Burn Index (CBI)) to quantify burn severity from strata level to total site level, yet suffering from surveyors' subjective interpretation across site conditions. High-resolution passive remote sensing allows for more objective assessment based on the strong relation between fire damages and spectral features. Importantly, burn severity generally characterizes differences between forest overstory and understory layers due to their discrepancies in vegetation structures and environmental conditions. Spatially explicit mapping of strata-level burn severity is vital for post-fire forest management and ecological function evaluation. Until now, almost no available spaceborne remote sensing method can concurrently assess overstory and understory burn severity over heterogeneous forests. In this study, we proposed a Hybrid Composite Burn Index (HCBI) that comprehensively indicates the fire-induced spectral and structural changes along the vertical profile by integrating spectral and waveform attributes from both active and passive remote sensing data. Firstly, we introduced two remote sensing-based rating factors namely relative spectral change (RSC) and relative waveform change (RWC), and established HCBI through weighting and scoring the rating factors. Subsequently, we evaluated the effectiveness and generality of overstory, understory, and total site HCBI based on various pairs of simulated remote sensing data of pre-fire and post-fire forest scenes. Thirdly, we derived the spatially discontinuous maps of overstory, understory, and total site HCBI of the Xiushan fire site in Great Xing'an Mountain using WorldView-2 (WV-2) multispectral imagery and Global Ecosystem Dynamics Investigation (GEDI) full-waveform LiDAR data. Finally, we produced wall-to-wall HCBI maps using multiple predictive variables from pre-fire and post-fire Sentinel-2 MultiSpectral Instrument (MSI) images with a Random Forest (RF) algorithm. The predicted overstory, understory, and total site HCBI were validated by field-surveyed CBI. The assessment results based on simulation data showed HCBI was sensitive to the fire damage regardless of burn severity levels and vegetation cover levels (R2 of larger than 0.97 and RMSE of <0.15). The prediction results based on RF models achieved reliable HCBI of the total site, overstory, and understory levels (R2 of around 0.85 and RMSE of around 0.30). We found the wall-to-wall maps of HCBI captured the subtle horizontal and vertical variation even in the case of understory burn alone. We concluded that the newly proposed HCBI can advance the remote sensing-based assessment of stratified burn severity, offering opportunities for making fine-scale forest management decisions. • HCBI was proposed to quantify overstory, understory and total site burn severity. • Remote sensing-based assessment form relating rating factors to HCBI was established. • HCBI was consistent with reference unbiased CBI using synthetic data from 3D model. • HCBI maps were produced to explore the stratified burn severity heterogeneity. [ABSTRACT FROM AUTHOR]

Traditional topology optimization methods often introduce weak artificial material to mimic voids to avoid the singularity of the global stiffness matrix and carry out topology optimization with a fixed finite element (FE) mesh. This treatment, however, may not only increase the computational cost for structural analysis but also lead to unfavorable numerical instabilities, especially when large deformations and dynamic/buckling behaviors are involved. In the present work, a new meshless moving morphable component-based method (ML-MMC), which structural analysis is carried out only on the solid region occupied by components, is proposed. In this approach, the coupling of discrete components is achieved through the adaptively constructed influence domain of the meshless shape function. Therefore, the singularity problem of the stiffness matrix can be naturally avoided without introducing weak artificial material. Compared with traditional methods, the number of degrees of freedoms (DOFs) can be reduced substantially under this treatment. The effectiveness of the proposed approach is also illustrated by some representative examples. [ABSTRACT FROM AUTHOR]

The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system. A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters. [ABSTRACT FROM AUTHOR]

Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands. [ABSTRACT FROM AUTHOR]

• A novel colour mixture analysis (CMA) method was proposed for mapping FVC. • Gaussian endmembers were derived from UAV proximal sensing RGB images. • CMA method can minimize the mixed pixel effect in close-range UAV RGB images. • CMA is robust to variations of resolution, vegetation coverage and illumination. Remote sensing via unmanned aerial vehicles (UAVs) is becoming a very important tool for augmenting traditional spaceborne and airborne remote sensing techniques. Commercial RGB cameras are often the payload on UAVs, because they are inexpensive, easy to operate and require little data processing. RGB images are increasingly being used for mapping of fractional vegetation cover (FVC). However, the presence of significantly mixed pixels in close-range RGB images prevents the accurate estimation of FVC. Even where pixel unmixing is applied, limited quantitative spectral information and colour variability within these images could lead to profound errors and uncertainties. This paper proposes a colour mixture analysis (CMA) method based on the Hue-Saturation-Value (HSV) colour space to alleviate the above-mentioned concerns, thereby improving the accuracy and efficiency of FVC estimation from UAV-captured RGB images. First, the a priori colour information of the pure vegetation and background endmembers are extracted from the Hue channel of the UAV proximal sensing images, obviating ground-based image capture and the attendant cost and inconvenience. Second, the relationship between the probability distribution of mixed pixels and that of the two endmembers is estimated. Finally, we estimate FVC from UAV remote sensing images with a maximum a posteriori parameter (MAP) estimator. Two UAV-captured RGB image datasets and a synthetic RGB image dataset were used to test the new method. CMA was compared with three other FVC estimation algorithms, namely, FCLS, HAGFVC and LAB2. The FVC estimates by CMA were found to be highly accurate, with root mean squared errors (RMSE) of less than 0.007 and mean absolute error (MAE) of less than 0.01 for both field datasets. The accuracy was shown to be superior to that of all three algorithms. A comprehensive analysis of the estimation accuracy under various spatial resolutions and vegetation cover levels was conducted using both field and synthetic datasets. Results show that the CMA method can robustly and accurately estimate FVC across the full range of vegetation coverage and various resolutions. Uncertainty and sensitivity analysis of colour variability due to heterogeneity and shadow were also tested. Overall, CMA was shown to be robust to variation in colour and illumination. [ABSTRACT FROM AUTHOR]

Accurate estimates of fractional vegetation cover (FVC) using remotely sensed images collected using unmanned aerial vehicles (UAVs) offer considerable potential for field measurement. However, most existing methods, which were originally designed to extract FVC from ground-based remotely sensed images (acquired at a few meters above the ground level), cannot be directly used to process aerial images because of the presence of large quantities of mixed pixels. To alleviate the negative effects of mixed pixels, we proposed a new method for decomposing the Gaussian mixture model and estimating FVC, namely, the half-Gaussian fitting method for FVC estimation (HAGFVC). In this method, the histograms of pure vegetation pixels and pure background pixels are firstly fit using two half-Gaussian distributions in the Commission Internationale d’Eclairage (CIE) L*a*b* color space. Then, a threshold is determined based on the parameters of Gaussian distribution to generate a more accurate FVC estimate. We acquired low-altitude remote-sensing (LARS) images in three vegetative growth stages at different flight altitudes over a cornfield. The HAGFVC method successfully fitted the half-Gaussian distributions and obtained stable thresholds for FVC estimation. The results indicate that the HAGFVC method can be used to effectively and accurately derive FVC images, with a small mean bias error (MBE) and with root mean square error (RMSE) of less than 0.04 in all cases. Comparatively, other methods we tested performed poorly (RMSE of up to 0.36) because of the abundance of mixed pixels in LARS images, especially at high altitudes above ground level (AGL) or in the case of moderate vegetation coverage. The results demonstrate the importance of developing image-processing methods that specially account for mixed pixels for LARS images. Simulations indicated that the theoretical accuracy (no errors in fitting the half-Gaussian distributions) of the HAGFVC method reflected an RMSE of less than 0.07. Additionally, this method provides a useful approach to efficiently estimating FVC by using LARS images over large areas. [ABSTRACT FROM AUTHOR]

Testing procedures for assessing whether two stationary and independent linear processes with unequal lengths have the same spectral densities or same auto-covariance functions are investigated. New test statistics are proposed based on the difference of the two wavelet-based estimates of the two spectral densities. The asymptotic normal distributions of the empirical wavelet coefficients are derived based on Bartlett type approximation of a quadratic form with dependent variables by the corresponding quadratic form with independent and identically distributed (i.i.d.) random variables. The limit distributions of the proposed test statistics are derived from those asymptotic results, and they asymptotically follow known chi-square distributions. The advantage of those new procedures is that those test statistics are constructed very simply and can be used for two time series with arbitrary lengths. The performance of those new tests is compared with some recent test statistics, with respect to their exact levels and powers. Simulation studies show that our proposed tests are very comparable to the current tests. [ABSTRACT FROM AUTHOR]

We consider wavelet-based non linear estimators, which are constructed by using the thresholding of the empirical wavelet coefficients, for the mean regression functions with strong mixing errors and investigate their asymptotic rates of convergence. We show that these estimators achieve nearly optimal convergence rates within a logarithmic term over a large range of Besov function classesBsp, q. The theory is illustrated with some numerical examples. A new ingredient in our development is a Bernstein-type exponential inequality, for a sequence of random variables with certain mixing structure and are not necessarily bounded or sub-Gaussian. This moderate deviation inequality may be of independent interest. [ABSTRACT FROM AUTHOR]

Test procedures for assessing whether two stationary and independent time series with unequal lengths have the same spectral density (or same auto-covariance function) are investigated. A new test statistic is proposed based on the wavelet transform. It relies on empirical wavelet coefficients of the logarithm of two spectral densities' ratio. Under the null hypothesis that two spectral densities are the same, the asymptotic normal distribution of the empirical wavelet coeffcients is derived. Furthermore, these empirical wavelet coefficients are asymptotically uncorrelated. A test statistic is proposed based on these results. The performance of the new test statistic is compared to several recent test statistics, with respect to their exact levels and powers. Simulation studies show that our proposed test is very comparable to the current test statistics in most cases. The main advantage of our proposed test statistic is that it is constructed very simply and is easy to implement. [ABSTRACT FROM AUTHOR]

In this paper, we consider tests for assessing whether two stationary and independent time series have the same spectral densities (or same autocovariance functions). Both frequency domain and time domain test statistics for this purpose are reviewed. The adaptive Neyman tests are then introduced and their performances are investigated. Our tests are adaptive, that is, they are constructed completely by the data and do not involve any unknown smoothing parameters. Simulation studies show that our proposed tests are at least comparable to the current tests in most cases. Furthermore, our tests are much more powerful in some cases, such as against the long orders of autoregressive moving average (ARMA) models such as seasonal ARMA series. [ABSTRACT FROM PUBLISHER]

Hall et al. (1999) proposed block-thresholding methods to estimate mean regression functions with independent random errors. They showed that block-thresholded wavelet estimators attain minimax-optimal convergence rates when the mean functions belong to a large class of functions that involve a wide variety of irregularities, including chirp and Doppler functions, and functions with jump discontinuities. In this article, we show that block-thresholded wavelet estimators still attain minimax convergence rates when the mean functions belong to a wide range of Besov classes [image omitted] (where s > 1/p, p ≥ 1 and q ≥ 1) with long-memory Gaussian errors. Therefore, in the presence of long-memory Gaussian errors, wavelet estimators still provide extensive adaptivity. [ABSTRACT FROM AUTHOR]

• A topology optimization approach of stiffened plate structure with various cross-sectional properties of stiffener is proposed. • The geometric characteristics of stiffeners can be conveniently controlled using Moving Morphable Components description. • Based on EFG method, the dynamic connection between the plate and the beam node can be realized directly. • Compared with the purely solid optimization model, the proposed method can realize the topology optimization of the stiffened plate structure in an efficient and accurate way. In the present work, a Moving Morphable Components (MMC)-based approach is proposed for topological design of stiffened plate structures, where the stiffeners are used as the building blocks of optimization. Unlike traditional approaches where three-dimensional solid model is often adopted for numerical analysis, in the proposed approach, beam model and plate model are adopted for the modeling of the stiffeners and base plate, respectively, in the context of Element-Free Galerkin (EFG) method. This treatment can not only reduce the computational cost associated with structural analysis substantially but also capture the concerned structural responses more accurately. Several numerical examples are provided to demonstrate the effectiveness and efficiency of the proposed approach. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: We consider block thresholding wavelet-based density estimators with randomly right-censored data and investigate their asymptotic convergence rates. Unlike for the complete data case, the empirical wavelet coefficients are constructed through the Kaplan–Meier estimators of the distribution functions in the censored data case. On the basis of a result of Stute [W. Stute, The central limit theorem under random censorship, Ann. Statist. 23 (1995) 422–439] that approximates the Kaplan–Meier integrals as averages of i.i.d. random variables with a certain rate in probability, we can show that these wavelet empirical coefficients can be approximated by averages of i.i.d. random variables with a certain error rate in . Therefore we can show that these estimators, based on block thresholding of empirical wavelet coefficients, achieve optimal convergence rates over a large range of Besov function classes , , and nearly optimal convergence rates when . We also show that these estimators achieve optimal convergence rates over a large class of functions that involve many irregularities of a wide variety of types, including chirp and Doppler functions, and jump discontinuities. Therefore, in the presence of random censoring, wavelet estimators still provide extensive adaptivity to many irregularities of large function classes. The performance of the estimators is tested via a modest simulation study. [Copyright &y& Elsevier]

Abstract: We consider the estimation of non-parametric regression function with long memory data and investigate the asymptotic rates of convergence of wavelet estimators based on block thresholding. We show that the estimators achieve optimal minimax convergence rates over a large class of functions that involve many irregularities of a wide variety of types, including chirp and Doppler functions, and jump discontinuities. Therefore, in the presence of long memory noise, wavelet estimators still provide extensive adaptivity to many irregularities of large function classes. [Copyright &y& Elsevier]

Abstract: Wavelet-based density estimators with randomly right-censored data are considered. We investigate the asymptotic rates of convergence of estimators based on thresholding of empirical wavelet coefficients. Unlike the complete data case, the empirical wavelet coefficients are constructed through the Kaplan–Meier estimators of the distribution functions. It turns out that these coefficients can be approximated by an average of i.i.d. random variables with a certain error rate. We show that the estimators achieve nearly optimal minimax convergence rates within logarithmic terms over a large range of Besov function classes , a feature not available for linear estimators when . [Copyright &y& Elsevier]

We provide an asymptotic expansion for the mean integrated squared error (MISE) of non-linear wavelet-based density estimators with randomly censored data. Our technique is facilitated by a result of Stute (Ann. Statist. 23 (1995) 422) that approximates the Kaplan–Meier integrals by an average of i.i.d. random variables with a certain rate. We show this MISE expansion, when the underlying survival density function and censoring distribution function are only piecewise smooth, is the same as analogous expansion for the kernel density estimators. However, for the kernel estimators, this MISE expansion holds only under the additional smoothness assumption. [Copyright &y& Elsevier]

This paper discusses the asymptotic behavior of Koul''s minimum distance estimators of the regression parameter vector in linear regression models with long memory moving average errors, when the design variables are known constants. It is observed that all these estimators are asymptotically equivalent to the least-squares estimator in the first order. [Copyright &y& Elsevier]

We study the estimation of a hazard rate function based on censored data by non-linear wavelet method. We provide an asymptotic formula for the mean integrated squared error (MISE) of nonlinear wavelet-based hazard rate estimators under randomly censored data. We show this MISE formula, when the underlying hazard rate function and censoring distribution function are only piecewise smooth, has the same expansion as analogous kernel estimators, a feature not available for the kernel estimators. In addition, we establish an asymptotic normality of the nonlinear wavelet estimator. [ABSTRACT FROM AUTHOR]

Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red–green–blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling. [ABSTRACT FROM AUTHOR]

Terrestrial LiDAR (TLS) instruments have great capability to describe canopy structure with unprecedented levels of detail in plot-scale forest measurement. However, in most cases, only parts of the trees within the sample plot are scanned due to occlusion effects from the other trees in the direction of the laser beam. These obstructions cause incomplete observation and missing features, resulting in the underestimation of tree attributes. Typical data acquisition with multi-scan mode seeks to mitigate occlusion effect and then provide almost full coverage of plot. The density of scan stations is determined by plot size, the density of plants and the metrics to be measured. Currently, the determination of density and locations of multiple scans depends on surveyor's experience and is lack of scientific protocols, generally, causing data redundancy and low efficiency of measurement, e.g. time cost in moving devices and scan registration. In fact, the occlusion effect primarily comes from the unknown complex spatial pattern of trees in natural forest conditions. UAVs are able to map the spatial locations of trees and are expected to provide basic knowledge for the optimization of TLS sampling pattern in forest conditions. The focus of this study is on strategies to derive multi-scan locations over forest plots. A novel chain-mode optimization of TLS sampling pattern was designed to maximize visibility of trees with minimum scans, hence improving the data quality and the efficiency of measurement. We proposed three basic optimization rules: (1) scan candidates prefer to locate on those positions undergoing low occlusion effect; (2) cumulative angular sampling (CAS) of each tree scanned from several directions satisfies the requirement user defined; (3) scan locations are uniformly distributed. In the implementation, 2D visibility analysis is firstly conducted for all the trees, simplified as circles in a plot, based on tree location map derived from UAV images. This step aims to find locally optimal scan locations following the first rule. Secondly, the CAS of each tree is geometrically computed. This step is devoted to select those trees requiring more scans to meet user level following the second rule. Thirdly, the former two steps are consecutively repeated in consideration of the third rule, only for those selected trees, until the CAS of each tree meets the user's requirements. Through two simulated forest plots where trees respectively distributed follow a spatial random pattern and a row-spaced pattern, the proposed chain-mode scans yielded comparable data quality with regular scans whereas the efficiency is improved by up to 30%. It is expected that this chain-mode optimization method can be applied in more forest experiments, especially in long-term TLS measurements. [ABSTRACT FROM AUTHOR]

Aims: Robot-assisted total hip arthroplasty (rTHA) boasts superior accuracy in implant placement, but there is a lack of effective assessment in perioperative management in the context of enhanced recovery after surgery (ERAS). This study aimed to compare the effectiveness and safety of rTHA versus conventional total hip arthroplasty (cTHA) in ERAS-managed patients. Methods: In this prospective trial, a total of 60 eligible patients aged between 18 and 80 years were randomly divided into two groups to undergo either rTHA or cTHA. The primary outcomes included blood loss parameters. Secondary outcomes were the duration of the operation, surgical time, WOMAC pain score, WOMAC stiffness score, WOMAC physical function score, Harris score, and postoperative complications. Results: The study cohort analyzed 59 eligible participants, 30 of whom underwent rTHA and 29 of whom underwent cTHA. Analysis could not be conducted for one patient due to severe anemia. Notably, the cTHA group had a significantly shorter surgical time than the rTHA group (69.49 ± 18.97 vs. 104.20 ± 19.63 min, P < 0.001). No significant differences were observed between the rTHA and cTHA groups for blood loss parameters, including total blood loss (1280.30 ± 404.01 vs. 1094.86 ± 494.39 ml, P = 0.137) and drainage volume (154.35 ± 121.50 vs. 159.13 ± 135.04 ml, P = 0.900), as well as intraoperative blood loss (126.67 ± 38.80 vs. 118.52 ± 60.68 ml, P = 0.544) and hidden blood loss (982.43 ± 438.83 vs. 784.00 ± 580.96 ml, P = 0.206). Only one patient in the cTHA group required allogeneic blood transfusion. At 3 months postoperatively, both groups showed improvements in WOMAC pain score, WOMAC stiffness score, WOMAC physical function score, and Harris score, with no significant differences found between the two groups. Few complications were reported in both groups without significant differences. Conclusions: Despite the longer surgical time, rTHA did not negatively affect blood loss, pain, or functional recovery or lead to an increased risk of complications in ERAS-managed patients, suggesting that rTHA can be safely and effectively incorporated into an ERAS program for primary THA. [ABSTRACT FROM AUTHOR]

Understanding post-fire forest recovery is critical to the study of forest carbon dynamics. Many previous studies have used multispectral imagery to estimate post-fire recovery, yet post-fire forest structural development has rarely been evaluated in the Great Xing'an Mountain. In this study, we extracted the historical fire events from 1987 to 2019 based on a classification of Landsat imagery and assessed post-fire forest structure for these burned patches using Global Ecosystem Dynamics Investigation (GEDI)-derived metrics from 2019 to 2021. Two drivers were assessed for the influence on post-fire structure recovery, these being pre-fire canopy cover (i.e., dense forest and open forest) and burn severity levels (i.e., low, moderate, and high). We used these burnt patches to establish a 25-year chronosequence of forest structural succession by a space-for-time substitution method. Our result showed that the structural indices suggested delayed recovery following the fire, indicating a successional process from the decomposition of residual structures to the regeneration of new tree species in the post-fire forest. Across the past 25-years, the dense forest tends toward greater recovery than open forest, and the recovery rate was faster for low severity, followed by moderate severity and high severity. Specifically, in the recovery trajectory, the recovery indices were 21.7% and 17.4% for dense forest and open forest, and were 27.1%, 25.8%, and 25.4% for low, moderate, and high burn severity, respectively. Additionally, a different response to the fire was found in the canopy structure and height structure since total canopy cover (TCC) and plant area index (PAI) recovered faster than relative height (i.e., RH75 and RH95). Our results provide valuable information on forest structural restoration status, that can be used to support the formulation of post-fire forest management strategies in Great Xing'an Mountain. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is one of the most destructive forest diseases that has led to rapid wilting and mortality in susceptible host pine trees. Spatially explicit detection of pine wood nematode (PWN)-induced infestation is important for forest management, policy making, and practices. Previous studies have mapped forest disturbances in response to various forest diseases and/or insects over large areas using remote-sensing techniques, but these efforts were often constrained by the limited availability of ground truth information needed for the calibration and validation of moderate-resolution satellite algorithms in the process of linking plot-scale measurements to satellite data. In this study, we proposed a two-level up-sampling strategy by integrating unmanned aerial vehicle (UAV) surveys and high-resolution Radarsat-2 satellite imagery for expanding the number of training samples at the 30-m resampled Sentinel-1 resolution. Random forest algorithms were separately used in the prediction of the Radarsat-2 and Sentinel-1 infestation map induced by PWN. After data acquisition in Muping District during August and September 2021, we first verified the ability of a deep-learning-based object detection algorithm (i.e., YOLOv5 model) in the detection of infested trees from coregistered UAV-based RGB images (Average Precision (AP) of larger than 70% and R2 of 0.94). A random forest algorithm trained using the up-sampling UAV infestation map reference and corresponding Radarsat-2 pixel values was then used to produce the Radarsat-2 infestation map, resulting in an overall accuracy of 72.57%. Another random forest algorithm trained using the Radarsat-2 infestation pixels with moderate and high severity (i.e., an infestation severity of larger than 0.25, where the value was empirically set based on a trade-off between classification accuracy and infection detectability) and corresponding Sentinel-1 pixel values was subsequently used to predict the Sentinel-1 infestation map, resulting in an overall accuracy of 87.63%, where the validation data are Radarsat-2 references rather than UAV references. The Sentinel-1 map was also validated by independent UAV surveys, with an overall accuracy of 76.30% and a Kappa coefficient of 0.45. We found that the expanded training samples by the integration of UAV and Radarsat-2 strengthened the medium-resolution Sentinel-1-based prediction model of PWD. This study demonstrates that the proposed method enables effective PWN infestation mapping over multiple scales. [ABSTRACT FROM AUTHOR]

Monitoring crops with high spatio-temporal resolution satellites provides valuable observations to ensure food security in the global change context. This study focuses on estimating the Green Area Index (GAI) to monitor wheat crops with a spatial resolution of 3 m and daily satellite observations from the SuperDove constellation. With an easier access to large training datasets of ground GAI measurements, and the improvement of the realism of radiative transfer model simulations, the choice of the optimal approach (data-driven or model-driven) constitutes a key question when retrieving GAI from satellite observations. This study compares a data-driven and a model-driven approach to estimate GAI from the SuperDove satellites. Both approaches are based on Gaussian Process Regression (GPR) machine learning techniques. The data-driven approach uses over 300 ground GAI measurements collected from 12 sites in China and France, each with 20 to 51 contrasting plots. The model-driven approach uses 10,000 simulations of top of canopy reflectance and the corresponding GAI values generated by the LESS radiative transfer model applied to 3D scenes built with the ADEL-Wheat (Architectural model of Development based on L-systems) model. Results confirm that the SuperDove reflectance are reliable and consistent with Sentinel-2 values. When estimating GAI using GPR with SuperDove top of canopy reflectance, the model-driven approach (R2 = 0.83, RMSE = 0.80, Accuracy = 0.01 and Precision = 0.80) generally outperforms the data-driven approach (R2 = 0.80, RMSE = 0.88, Accuracy = −0.13 and Precision = 0.87), except for small GAI values. In-silico experiments show that the uncertainties in the ground-measured GAI and the size and diversity of the training datasets limit the data-driven approach. In contrast, the model-driven approach is mostly constrained by the realism of the reflectance simulations, particularly for low GAI values. Two ensemble solutions based on the weighted average of the two previous approaches are then proposed: the global ensemble solution (R2 = 0.86, RMSE = 0.75, A = −0.06 and P = 0.74) where the weight is assumed independent from the GAI values, and the adaptive ensemble solution (R2 = 0.85, RMSE = 0.76, A = −0.08 and P = 0.76) where the weight depends on the GAI values. Both solutions perform similarly, improving both data-driven and model-driven approaches. Finally, applying both solutions to monitor wheat plots along the growth cycle allows clear differentiation of nitrogen modalities and cultivar effects. However, a minimum plot size of 12 m × 12 m (4 × 4 pixels) is recommended to minimize the co-registration errors and increase estimate precision. • Data-driven and model-driven approaches for GAI retrieval of wheat from PlanetScope. • The model-driven approach outperforms the data-driven one except for low GAI values. • Both approaches were combined in an ensemble solution which improves GAI estimates. • The proposed methodology allows accurate and precise monitoring of wheat crops. [ABSTRACT FROM AUTHOR]

This paper proposes a procedure of synthetic detection for the location of a change point and outliers in bilinear time series models with a change after an unknown time point. Based on Bayesian framework, we first derive the conditional posterior distribution of the change point and from that distribution estimate the position of the change point. Then we use these results to detect the outliers in the time series before and after that change point via Gibbs sampler algorithm. Our simulation studies show that the proposed procedure is effective. [ABSTRACT FROM PUBLISHER]

The traditional drying method, sun drying, for Chrysanthemum morifolium Ramat. cv. Hang-ju (Compositae) (HJ) is widely replaced by sulphur fumigation (SF), which has an unknown effect on its efficacy. To investigate protective effects of nonfumigated HJ (NHJ) and sulphur-fumigated HJ (SHJ) water extracts against oxidative stress and lipid peroxidation. Sprague-Dawley rats were administered high-fat diet to induce hyperlipidaemia and randomly divided into eight groups (n = 6): control, fenofibrate, NHJ and SHJ extracts (1, 2 or 4 g crude drugs/kg/d; intragastric administration for 8 weeks). Serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), superoxide dismutase (SOD) and malondialdehyde (MDA) levels were detected. Human umbilical vein endothelial cells (HUVECs) were treated with NHJ and SHJ extracts (50, 100 or 200 μg/mL) for 24 h, followed by oxidized low-density lipoprotein (ox-LDL, 20 μg/mL) for 2 h in vitro. Cellular reactive oxygen species (ROS), SOD and MDA levels and apoptosis were evaluated. NHJ was more effective than SHJ in decreasing serum TG, TC, LDL-C, LDL/HDL and MDA while increasing serum HDL-C and SOD levels at high doses. SHJ (IC50=19.9 mg/mL) suppressed HUVEC growth stronger than NHJ (IC50=186.7 mg/mL). At 200 μg/mL, NHJ was more effective than SHJ in downregulating ROS and MDA levels, reducing HUVECs apoptosis rate and elevating SOD activity in ox-LDL-treated HUVECs. SF causes oxidative damage and attenuates antioxidative activity in ox-LDL-treated HUVECs, which promotes lipid peroxidation. SF is not recommended for processing HJ. [ABSTRACT FROM AUTHOR]

The training model for Southwest University's no-fee teacher education program has taken shape over several years. Based on a review of the documentation and interviews with administrators and no-fee preservice students from different specialties, this article analyzes Southwest University's no-fee teacher-training model in terms of three main areas-curriculum, practice, and activities. The analysis includes the three dimensions of student knowledge, skills, and emotions as specific expressions of the educational goals of Southwest University's no-fee teacher-training model, and also addresses the characteristics of the model. [ABSTRACT FROM AUTHOR]

Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R2 = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions. [ABSTRACT FROM AUTHOR]

A topology optimization approach for designing the layout of plate structures is proposed in this article. In this approach, structural mechanical behavior is analyzed under the framework of Kirchhoff plate theory, and structural topology is described explicitly by a set of moving morphable components. Compared to the existing treatments where structural topology is generally described in an implicit manner, the adopted explicit geometry/layout description has demonstrated its advantages on several aspects. Firstly, the number of design variables is reduced substantially. Secondly, the obtained optimized designs are pure black-and-white and contain no gray regions. Besides, numerical experiments show that the use of Kirchhoff plate element helps save 95–99% computational time, compared with traditional treatments where solid elements are used for finite element analysis. Moreover the accuracy of the proposed method is also validated through a comparison with the corresponding theoretical solutions. Several numerical examples are also provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • Revealing the succession rule of microbial community structure in Jiuyao fermentation. • Environmental factors drive the dynamic changes of dominant microorganisms in Jiuyao. • Revealing rule of dynamic changes in transcription levels of key enzymes in Jiuyao. The microbial ecosystem of fermented food is greatly disturbed by human activities.Jiuyao is important saccharification starter for brewing huangjiu. The interaction between environmental factors and microorganisms significantly affected the microbial community structure at different stages of Jiuyao manufacturing. This study combined environmental factor analysis and high-throughput sequencing technology to comprehensively analyze the specific changes of microbial community and environmental factors in each fermentation stage of Jiuyao production and their correlation. The results showed that the activities of liquefaction enzyme, glycosylation enzyme and acid protease reached the highest value on the 8 th day (192 h) after the beginning of fermentation, and the cellulase activity reached the highest value at the end of fermentation. Pediococcus （37.5 %-58.2 %）, Weissella （9.2 %-27.0 %） and Pelomonas （0.1 %-12.1 %） were the main microbial genera in the genus bacteria, and Saccharomycopsis （37.1 %-52.0 %）, Rhizopus （12.5 %-31.0 %） and Saccharomyces （4.0 %-20.5 %） were the main microbial genera in the genus fungi. The results of correlation analysis showed that the microbial communities in Jiuyao were closely related to environmental factors. Most microbial communities were positively correlated with temperature, but negatively correlated with ambient humidity, CO 2 concentration, acidity and water content of Jiuyao. In addition, the transcription levels of enzymes related to microbial glucose metabolism in Jiuyao were higher in the late stage of Jiuyao fermentation. Interestingly, these enzymes had high transcription levels in fungi such as Saccharomycopsis , Rhizopus and Saccharomyces , as well as in bacteria such as Pediococcus and Lactobacillus. This study provides a reference for revealing the succession rule of microbial community structure caused by environmental factors during the preparation of Jiuyao in Shaoxing Huangjiu. [ABSTRACT FROM AUTHOR]

Abstract Three-dimensional (3D) radiative transfer modeling of the transport and interaction of radiation through earth surfaces is challenging due to the complexity of the landscapes as well as the intensive computational cost of 3D radiative transfer simulations. To reduce computation time, current models work with schematic landscapes or with small-scale realistic scenes. The computer graphics community provides the most accurate and efficient models (known as renderers) but they were not designed specifically for performing scientific radiative transfer simulations. In this study, we propose LESS, a new 3D radiative transfer modeling framework. LESS employs a weighted forward photon tracing method to simulate multispectral bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km2) spectral images. The backward path tracing also has been extended to simulate thermal infrared radiation by using an on-the-fly computation of the sunlit and shaded scene components. This framework is achieved through the development of a user-friendly graphic user interface (GUI) and a set of tools to help construct the landscape and set parameters. The accuracy of LESS is evaluated with other models as well as field measurements in terms of directional BRFs and pixel-wise simulated image comparisons, which shows very good agreement. LESS has the potential in simulating datasets of realistically reconstructed landscapes. Such simulated datasets can be used as benchmarks for various applications in remote sensing, forestry investigation and photogrammetry. Highlights • Forward photon tracing and backward path tracing are implemented in LESS. • LESS has been extended to simulate thermal infrared images. • LESS supports simulating large-scale spectral images based on 3D landscapes. • LESS provides an easy-to-use GUI and related tools to construct the 3D scene [ABSTRACT FROM AUTHOR]

Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method. [ABSTRACT FROM AUTHOR]

Ethnopharmacological relevance The dried roots of Sophora flavescens Ait. (Leguminosae) is traditionally used as antipyretic medicine to reduce inflammation. It is well known that alkaloids and flavonoids are the main constituents of S. flavescens . However, the clinical researches and applications of S. flavescens is mainly based on its water-extracted alkaloids, its flavonoids may still remain in residues and have been underused. With development and manufacturing of S. flavescens in recent years, more herb residues are being produced. Since they are typically treated as waste and dumped openly in landfill sites, which can cause pollution, there is a great need to explore these wastes as recyclable resources and increase their added value. To date, whether other bioactive components would be found in the residues of S. flavescens is still unknown. If the extraction method of these active ingredients was established, the residues of S. flavescens could be turned from the harm to a benefit and make great sense of the comprehensive utilization of S. flavescens resources. This study aimed to establish an extraction method of the residues of S. flavescens and investigate the anti-inflammatory effect of it both in vivo and in vitro . Materials and methods Dried S. flavescens were decocted with distilled water firstly, then the residues were powdered and extracted with ethyl acetate by using ultrasonic wave. HPLC was utilized to analyze the chemical constituents of the water extracts of S. flavescens (WSF) and the ethyl acetate extracts of residues of S. flavescens (RSF). In vivo , the anti-inflammatory effect of WSF and RSF were evaluated using the xylene-induced auricle edema, acetic acid-induced peritoneal permeability and carrageenan-induced hind paw edema methods. In vitro, the inhibitory activities of WSF and RSF on NO, TNF-α, IL-6 and MCP-1 production of LPS-treated RAW264.7 cells were measured. Results The major ingredients of RSF were flavonoids, while WSF almost had no flavonoids. In vivo , WSF and RSF (200 mg/kg) could significantly inhibit the edema in the xylene-induced mice auricle edema and carrageenan-induced hind paw edema as well as the peritoneal permeability increased by acetic acid. They can also lower production levels of PGE 2 in inflamed paw tissues. In vitro experimental results showed that RSF (25-100 μg/mL) could significantly inhibit the release of pro-inflammatory cytokines NO, TNF-α, IL-6 and MCP-1 on LPS-induced RAW264.7 cells. The in vitro suppress effect of WSF had no dose-response relationship. Conclusions The residues of S. flavescens had obvious flavonoids with anti-inflammatory activity. This study provided evidence for the reuse of residues from S. flavescens in the food additive, medicine and cosmetic industries. [ABSTRACT FROM AUTHOR]

A meshless formulation for fracture analysis of stiffened plates is introduced in this paper. Based on an improved meshless model of stiffened plates proposed by the authors, in which flat plate and ribs are combined by implementing the displacement compatibility conditions between them, a crack is introduced by the diffraction method. The expanded basis function and the weight function based on t −1 distribution are employed. Inheriting the meshless advantages from the model, the ribs in our formulation can be set at any location on the flat plate, and the remeshing of the flat plate is naturally avoided when rib location changes. Some numerical examples are investigated by the proposed formulation and the commercial FEM software ANSYS, and the accuracy of the proposed formulation is verified. The effects of ribs on the displacement, stress and SIFs of the stiffened plates that have cracks are discussed. [ABSTRACT FROM AUTHOR]

• Microscopic spectroscopy imaging is explored for needle reflectance measurement. • This method measures bidirectional reflectance and is easy to be implemented. • Needle azimuth and specular reflection affect correlations to chlorophyll content. • The method can eliminate specular reflection from reflectance of conifer needles. Leaf reflectance is widely used to retrieve leaf chlorophyll content (Cab) and parameterize canopy radiative transfer models. Measurements of broadleaf reflectance are typically made by using integrating sphere devices, but the approach is generally limited in conifer needle measurements due to the narrow needle coverage relative to the sample port of the integrating sphere. In this study, we proposed a method to measure the bidirectional reflectance factor (BRF) of needles by integrating a hyperspectral imaging spectrometer and an optical microscope. Pure needle pixels can be easily extracted from hyperspectral images after microscope magnification. The method was first validated by the narrow strips of broad leaves as proxies of needles, resulting in the difference between the measured BRF of strips and the BRF of whole broad leaves in the visible bands (400–700 nm) and in the near-infrared bands (700–1000 nm) being 0.0043 and 0.019, respectively. The method was also indirectly verified by tracking the variation in the natural needle BRF with its Cab, resulting in the change in BRF being consistent with the change in Cab. In addition, linear relationships between the needle Cab and the classic vegetation indices (i.e. , the red-edge simple ratio and normalized difference vegetation index) calculated from the BRF were found. Apart from the above, we found that (1) the BRF of the abaxial side has a discrepancy with that of the adaxial side of needles, (2) the BRF of a needle varies with its azimuth, hence affecting the correlation between vegetation indices and Cab, and (3) filtering the specular reflection on the microsurfaces of the needle could enhance the correlation between the vegetation indices and needle Cab. The method is expected to accurately measure the BRF of needles and hence to improve the applications of needle optical properties in plant sciences, ecology, and remote sensing fields. [Display omitted] [ABSTRACT FROM AUTHOR]

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVI v ) and bare soil (NDVI s ), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVI v and NDVI s at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVI s and NDVI v can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVI v and NDVI s . The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVI v and NDVI s maps for FVC estimation on regional and global scales. [ABSTRACT FROM AUTHOR]

The clumping effect is the main issue causing the heterogeneity in vegetation canopies and the underestimation of leaf area index (LAI) obtained using indirect measurement methods. Significant efforts have been exerted to correct for the clumping effect and derive the true LAI. Recent research has shown that the fractal dimension (FD) is directly related to the clumping effect of foliage, yet practical methods are needed to calculate field estimates. Considering that widely used LAI applications such as digital hemispherical photography (DHP), tracing radiation and architecture of canopies (TRAC), and digital cover photography (DCP) estimate LAI with one-dimensional (1D) gap probability and gap size data, we propose a method to correct for the clumping effect using 1D FD. Resulting formulae describing the relationship between LAI, CI, and 1D FD were based on the box-counting method (BCM) and a binomial distribution model. Sixty-four simulated scenes including four RAdiation transfer Model Intercomparison (RAMI) actual canopies and field measurements from nine plots (four orchard plots and five coniferous forest plots) were used to validate the novel method. Results showed good agreement with reference LAI values for simulated scenes (R2 = 0.96 and RMSE = 0.35). The 1DFD method generated higher LAI estimates compared with the LAI measured using TRAC at the four orchard plots especially at high canopy closure, while its results were more consistent with LAI obtained by litter collection than those of comparable methods at coniferous forest plots (bias from −13.5% to 9.9% for DCP images, from −3.0% to 19.7% for DHP images, and from −3.8% to 17.0% for TRAC transects). Our validation efforts indicate that the method proposed herein corrects for the clumping effect of vegetated canopies more effectively with DCP images, DHP images, and TRAC measurement when compared with traditional indirect optical methods. The 1DFD method is expected to improve indirect measurement accuracy of LAI. • One-dimensional fractal dimension (1D FD) is explored for LAI field measurement. • A formula between 1D FD, LAI, and CI (clumping index) is rigorously deduced. • LAI and CI can be estimated using the formula, 1D FD, and gap fraction. • The new method corrects for clumping effect more properly than classical methods. • The novel method is suitable for many widely used LAI-measurement instruments. [ABSTRACT FROM AUTHOR]

Canopy radiative transfer (RT) modeling is critical for the quantitative retrieval of vegetation biophysical parameters and has been under intensive research over the decades. RT models of discontinuous canopies, such as three-dimensional (3D) RT models, posed challenges for the early one-dimensional (1D) hypothesis. Although 3D RT models have higher accuracy, theoretically, they suffer from two problems: detailed scene parameters and complex computational steps. To overcome these problems, the stochastic radiative transfer (SRT) theory, which is known to have the accuracy of 3D RT while being as simple as 1D RT, has been adapted from atmospheric research to the study of vegetation canopies. While the SRT model has been adopted into the operational production of vegetation parameters, its accuracy needs further improvement because of the insufficient consideration of hotspot effects. Additionally, the evaluation and validation of SRT process are still preliminary, which hinders its further development and application. To provide the community with missing information and a scientific basis for subsequent model improvement, we modified, evaluated, and validated the SRT model in this study. First, we proposed the new version of SRT model to better achieve the coupling of SRT process and hotspot effect by dividing the previous SRT into four subproblems. Then, we evaluated the performance of the modified SRT by comparing multiple intermediate variables in the SRT process with 3D computer simulations, and analyzed the model sensitivity to key input parameters as well as the spatial distribution and conservation of radiation energy. Our findings reconfirmed that the SRT theory can well describe the radiation regime of discontinuous canopies with balanced efficiency and accuracy. Moreover, the newly proposed coupling scheme of hotspot effect further improves the model performance in the hotspot regions. Finally, the unmanned aerial vehicle (UAV) observations served as a reference to validate the modeled canopy reflectance, which shows a high concordance. These results provide a detailed theoretical basis for applications and further improvements of the SRT model. • Stochastic radiative transfer (SRT) model was modified in terms of hotspot effects. • A comprehensive evaluation of the SRT process was achieved. • The 3D RT model based on computer simulations was used to evaluate the SRT model. • The SRT model was validated using UAV measurements. • Provided a detailed basis for the retrieval of vegetation biophysical parameters. [ABSTRACT FROM AUTHOR]

Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study. [ABSTRACT FROM AUTHOR]