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Mental disorders constitute a global public health problem worldwide and occurs in childhood and lasts to adulthood. The purpose of this study was to explore the association between childhood violence exposure and currently diagnosed depression and anxiety in children and adolescents and investigate whether the association differs by sex and age. This cross-sectional study used data from 2017 to 2021 National Survey of Children's Health (NSCH). Childhood violence exposure and anxiety or depression of children were reported from caregivers. Logistic regression analyses and subgroup analyses were conducted to assess the association between the childhood violence exposure and anxiety and depression. This study included 113,400 participants aged 6 to 17 years (weighted mean [SD] age, 11.5 [3.4] years; 54,539 females [weighted proportions, 48.9 %]). 9603 of them (weighted proportions, 9.0 %) had childhood violence exposure. Childhood violence exposure was associated with higher risks of anxiety (adjusted risk difference: 7.8 %, 95%CI: 6.3–9.3 %; adjusted OR: 2.11, 95%CI: 1.87–2.39) and depression (adjusted risk difference: 6.2 %, 95%CI: 5.1–7.4 %; adjusted OR: 2.94, 95%CI: 2.54–3.41) after full adjustment. Stratified analyses demonstrated that the association appeared to be stronger in younger children and the association between violence exposure and anxiety was stronger in females than in males. In this cross-sectional study, childhood violence exposure was significantly associated with higher risks of anxiety and depression. The associations appeared to be stronger in younger and female children. Differences in sex and age-specific associations highlight the importance of building age-specific and sex-specific patterns of childhood violence prevention and intervention. • Childhood violence exposure was significantly associated with higher risks of anxiety and depression. • The associations appeared to be stronger in younger and female children. • Age-specific and sex-specific patterns of childhood violence prevention and intervention are essential. [ABSTRACT FROM AUTHOR]

The assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data and crop growth model. Based on two years of field experiment data, the remote sensing inversion model using assimilation intermediate variables, namely leaf area index (LAI) and leaf nitrogen accumulation (LNA), was constructed with an R2 greater than 0.80 and a low root-mean-square error (RMSE). The different data assimilation approaches showed that when the LAI and LNA variables were used together in the assimilation process (VLAI+LNA), better accuracy was achieved for LNA estimations than the assimilation process using single variables of LAI or LNA (VLAI or VLNA). Similar differences in estimation accuracy were found in the maize yield and grain protein content (GPC) simulations. When the LAI and LNA were both intermediate variables in the assimilation process, the estimation accuracy of the yield and GPC were better than that of the assimilation process with only one variable. In summary, these results indicate that two physiological and biochemical parameters of maize retrieved from hyperspectral data can be combined with the crop growth model through the assimilation method, which provides a feasible method for improving the estimation accuracy of maize LAI, LNA, GPC and yield. [ABSTRACT FROM AUTHOR]

Preserving the structural and stratigraphic discontinuities or edges is essential in seismic data processing and interpretation. According to several numerical experiments, it is obvious that random noise has a constant spectral density, whereas the structural features vary significantly within different frequency bands, which means that the ratio between the densities of noise and structural features varies significantly in different frequency bands. Therefore, we propose a method called adaptive hybrid diffusion to attenuate random noise, which utilizes a novel adaptive frequency‐based parameter. First, the adaptive hybrid diffusion method decomposes the seismic sections into several band‐limited portions using variational mode decomposition. These portions are called intrinsic mode functions, in which noise and structural energy have distinct differences. Subsequently, utilizing the adaptive frequency‐based parameter, each intrinsic mode function is divided into several monotonous portions that represent the noise or structural area. Afterwards, the total variation and L2 minimization algorithms are utilized separately to suppress the noise in different band‐limited monotonous areas. The algorithms are chosen dynamically, as the portion changes with the change in the adaptive parameter. Finally, these denoised portions are combined to obtain the denoised seismic section. Experimental results on synthetic and field seismic data showed that seismic noise is effectively suppressed by the adaptive hybrid diffusion method, with the edge details of seismic events well preserved. [ABSTRACT FROM AUTHOR]

• TIA had a relatively high recurrence rate in children with moyamoya disease. • 16% of 444 hemispheres experienced postoperative TIA after revascularization. • 7.7% of 444 hemispheres developed postoperative symptom progression. • Higher baseline blood pressure was risk factor predicting postoperative TIA. • High variability and drastic decline in MAP may predict symptom progression. This study aimed to investigate the association between intraoperative blood pressure and postoperative transient ischemic attacks (TIAs) in pediatric patients with moyamoya disease after indirect revascularization surgery. We retrospectively reviewed the medical records of patients with moyamoya disease younger than 15 years who underwent indirect revascularizations under general anesthesia from 2013 to 2019. Perioperative clinical factors and intraoperative blood pressure data were collected and analyzed. Univariate and multivariable mixed-effect logistic regressions were used to identify predictors of postoperative TIA and symptom progression. Among 444 hemispheres in 296 pediatric patients, 70 cases (16%) experienced postoperative TIAs within 2 weeks after surgery, and 34 cases (7.7%) developed postoperative symptom progression. Baseline mean blood pressure (MBP) (adjusted odds ratio, 1.261; 95% confidence interval, 1.037−1.528; P < 0.001), Average mean arterial pressure (MAP) (adjusted odds ratio, 1.137; 95% confidence interval, 1.052−1.228; P = 0.006), and Ipsilateral magnetic resonance angiography (MRA) score (adjusted odds ratio, 1.464; 95% confidence interval, 0.886−2.419; P = 0.012) were statistically significant risk factors for postoperative TIA. Average real variability of MAP (ARV-MAP), which were measures of intraoperative MAP variability (adjusted odds ratio, 4.731; 95% confidence interval, 1.419−15.257; P = 0.003) and maximum MAP declination (MaxD-MAP) (adjusted odds ratio, 1.271; 95% confidence interval, 1.013−1.520; P = 0.010) were statistically significant risk factors for postoperative symptom progression. Higher pre-induction baseline blood pressure and ipsilateral MRA score were independent risk factors predicting postoperative TIA in pediatric patients with moyamoya disease after indirect revascularization. The high variability and drastic decline in intraoperative MAP showed predictive value in postoperative symptom progression. [ABSTRACT FROM AUTHOR]

Introduction: Maternal pregnancy smoking has adverse perinatal outcomes and the relationship between maternal smoking and neonatal death has not been fully elucidated. We aimed to examine the risk of neonatal death in relation to maternal smoking and to quantify potential mediators of these associations. Methods: We did a population-based cohort study using Period Linked Birth-Infant Death data from 2016 to 2019 in the US National Vital Statistics System. The exposure was maternal smoking status. The main outcome was neonatal death. Association between maternal smoking and neonatal death was estimated through logistic regression. Mediation analysis was performed to assess the extent to which the association between maternal smoking and neonatal death was mediated by neonatal complications. Results: The final sample consisted of 14,717,020 mothers with live singleton births. The overall neonatal mortality rate was 2.2 per 1,000 live births. Maternal pregnancy smoking was associated with an increased risk of neonatal death {adjusted odds ratio (aOR, 1.33 [95% CI, 1.28–1.38]; p < 0.001)}, while smoking cessation during the whole pregnancy showed a comparable risk of neonatal death with nonsmokers (aOR, 1.06 [95% CI, 0.99–1.14]; p = 0.116). Mediation analysis indicated that the association between pregnancy smoking and neonatal death might be mainly mediated by preterm birth and low Apgar score at 5 min. Conclusions: Maternal pregnancy smoking, regardless of pregnancy trimester and intensity, was associated with increased risk of neonatal death. Efforts are needed for policymakers to promote smoking cessation before pregnancy, and professional perinatal care should be provided for those who smoked during pregnancy. [ABSTRACT FROM AUTHOR]

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model's performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields. [ABSTRACT FROM AUTHOR]

Soil texture is a key physical property that affects the soil's ability to retain moisture and nutrients. As a result, it is of extreme importance to conduct remote sensing monitoring of soil texture. Songnen Plain is located in the black soil belt of Northeast China. The development of satellite imagery in remote sensing technology enables the rapid monitoring of large areas. This study aimed to map the surface soil texture of cultivated land in Songnen Plain using Sentinel-2 images and Random Forest (RF) algorithm. We conducted this study by collecting 354 topsoil (0–20 cm) samples in Songnen Plain and evaluating the effectiveness of the bands and spectral indices of Sentinel-2 images and RF algorithm in predicting soil texture (sand, silt, and clay fractions). The results demonstrated that the 16 covariates were moderately and highly correlated with soil texture. And, Band11 of Sentinel-2 images could be used as the corresponding band of soil texture. For sand fraction, the Sentinel-2 images and RF algorithm's Coefficient of Determination (R2) and Root Mean Square Error (RMSE) were 0.77 and 10.48%, respectively, and for silt fraction, they were 0.75 and 9.38%. Sand fraction decreased from southwest to northeast in Songnen Plain, while silt and clay fractions increased. We found that the Songnen Plain was affected by water erosion and wind erosion, in the northeast and southwest, respectively, providing reference for the implementation of Conservation Tillage policies. The outcome of the study can provide reference for future soil texture mapping with a high resolution. [ABSTRACT FROM AUTHOR]

Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images. [ABSTRACT FROM AUTHOR]

• Remote sensing parameters using time series analysis: by employing time series analysis on Sentinel data, a comprehensive exploration of spectral and polarization responses was conducted, revealing correlations with maize damage severity. • Model for maize damage assessment: a model was developed using the Random Forest algorithm to monitor maize damage severity in different post-typhoon periods, generating maps depicting the distribution and magnitude of maize damage with improved monitoring accuracy. • Maize damage recovery from post-typhoons until maturity period: the study also assessed the recovery of maize damage during the period from typhoons to harvest, providing insights into the resilience and recovery trajectory of maize following severe typhoon events. • SAR and optical data fusion: the study highlighted the importance of SAR and optical data fusion in elucidating the dynamics of crop losses caused by typhoons, providing essential insights for proactive disaster risk reduction strategies in agriculture. • Post-typhoon maize damage impact factors: analysis of terrain and meteorological data revealed that post-typhoon maize disasters primarily occurred in low-lying and flat areas, with meteorological factors such as maximum wind speed and daily cumulative precipitation significantly impacting damage severity. The increasing frequency of typhoon events, attributed to global climate change, has significantly affected agricultural production, predominantly resulting in substantial negative consequences. Accurate and timely assessment of crop damage is crucial for understanding economic implications, devising effective agricultural strategies, and enhancing resilience amid mounting climate uncertainties. This study investigates the utility of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) data in tracking maize damage severity following typhoon events. By employing continuous field sampling techniques and conducting visual interpretation of high-resolution remote sensing imagery, sample sets representing a spectrum of maize damage severity were systematically established. The application of time series analysis on Sentinel data enables a comprehensive exploration of spectral and polarization responses, providing insights into the correlation with maize damage severity. Segmentation of the maize damage timeline into pre-disaster, disaster, and recovery periods, coupled with optimization of relevant feature parameters, was undertaken to bolster monitoring precision. Leveraging the Google Earth Engine (GEE) cloud platform, a Random Forest algorithm was used to develop a model for monitoring maize damage severity across different post-typhoon periods, yielding maps delineating the distribution and magnitude of maize damage in Northeast China. Results indicate that integrating spectral indices from the pre-disaster phase with backscatter variations of polarization bands during various post-typhoon periods enhances maize damage assessment. Maize damage severity is notably elevated during the disaster period, achieving an overall accuracy of 87.22 %. While mitigated during the recovery phase, localized exacerbation occurs in severely affected regions, yielding an overall accuracy of 88.54 %. Analysis incorporating terrain and meteorological data reveals that post-typhoon maize disasters predominantly occur in low-lying and flat areas, with meteorological factors, particularly maximum wind speed and daily cumulative precipitation, exerting significant influence on damage severity. This study underscores the critical role of SAR and optical data fusion in elucidating typhoon-induced crop damage dynamics, thereby providing essential insights for proactive mitigation strategies against future agricultural losses. [ABSTRACT FROM AUTHOR]

Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R2 of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R2 of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale. [ABSTRACT FROM AUTHOR]

• Hydrothermal conditions and DEM play great effect on digital soil color class mapping. • Stepwise classification improves the accuracy of soil color mapping. • Soil color has an obvious relationship with SOM. Soil color is the most readily available soil property, soil scientists often use the Munsell system to describe soil color and to quickly understand the physical, chemical, and biological properties of soil through soil color variation. Currently, soil color mapping studies often rely on indirect acquisition of Munsell color data from soil spectral information. As a result, there may be some discrepancies between the mapped colors and the colors perceived by humans. In this study, we collected 880 dry and 827 moist Munsell samples from the Northeast China to test the method developed for soil color mapping. We selected a set of covariates (climate, topography, evapotranspiration, regolith thickness, and remote sensing images) for digital soil color mapping, these covariates were related to soil formation or organic matter accumulation processes. Finally, the dry and moist topsoil color mapping with 30-m resolution was obtained for cropland in Northeast China. These covariates all played positive roles in the soil classification process, among which annual temperature mean (MAT), mean annual precipitation (MAP), land surface temperature (LST), and mean annual potential evapotranspiration (MAPET) had the highest contribution. We summarized the difficulties that usually occur in soil color mapping, and put forward a 'stepwise classification' method. According to the three-dimensional attributes of the Munsell system, the measured color data were divided into seven color groups (four dry and three moist color groups). Among all color groups, the highest accuracy of mapping validation was 0.69 and the lowest was 0.58. The kappa coefficient, usually affected by classification bias, was small (0.3). The color differences between measured and predicted colors were found to be 1.21 (dry color) and 1.36 (moist color). Compared to direct classification, the stepwise classification method improved classification accuracy and reduced mapping color differences. Our soil color mapping for two cases of dry and moist corresponded well with spectral color maps. The correlation between soil organic matter (SOM) content and soil color (dry and moist) was analyzed in 807 samples. The darker the soil color was, the higher the SOM content was. In summary, soil color holds the potential to serve as a surrogate for assessing cropland quality, particularly in evaluating SOM. [ABSTRACT FROM AUTHOR]

The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas. [ABSTRACT FROM AUTHOR]

Olivine formation is directly related to Mg/Fe content. It is also significant in estimating the geological evolution of the moon. In this study, an estimation model of relative Mg number (Fo#) for lunar olivine was presented through multiple linear regression statistics. Sinus Iridum, the Copernicus Crater, and the pyroclastic deposit in the volcanic vents in the southeast of Orientale Basin were selected as the study areas. Olivine distribution was surveyed, and the relative Fo# calculation of olivine was implemented based on Moon Mineralogy Mapper (M3) data. Results demonstrated that olivine in the crater wall of Sinus Iridum and the Copernicus Crater had relatively high Fo#, which reflected the primitive melt. However, the difference in olivine spectral features between Sinus Iridum and the Copernicus Crater indicated different crystallization modes. The olivine in the pyroclastic deposit in the volcanic vents in the southwest of Orientale Basin also presented high Fo#, which indicated that the olivine was formed via rapid cooling crystallization and was accompanied by volcanic glass substances. As a result, the olivine relative Fo# calculated from the estimation model exhibited an important constraint implication for explanation of its causes. [ABSTRACT FROM AUTHOR]

Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed. [ABSTRACT FROM AUTHOR]