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The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China. Presently, most remote sensing process models use the “biomass×harvest index (HI)” method to simulate regional-scale winter wheat yield. However, spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale. Time-series dry matter partition coefficients (Fr) can dynamically reflect the dry matter partition of winter wheat. In this study, Fr equations were fitted for each organ of winter wheat using site-scale data. These equations were then coupled into a process-based and remote sensing-driven crop yield model for wheat (PRYM-Wheat) to improve the regional simulation of winter wheat yield over the North China Plain (NCP). The improved PRYM-Wheat model integrated with the fitted Fr equations (PRYM-Wheat-Fr) was validated using data obtained from provincial yearbooks. A 3-year (2000–2002) averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination (R²=0.55) and lower root mean square error (RMSE=0.94 t ha–1) than PRYM-Wheat with a stable HI (abbreviated as PRYM-Wheat-HI), which had R² and RMSE values of 0.30 and 1.62 t ha–1, respectively. The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years (2013–2015). In conclusion, the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model, making it a useful tool for the simulation of regional winter wheat yield.

Oxidative stress and inflammation are crucial factors contributing to the occurrence and development of vascular dementia (VD). In a previous study, we demonstrated that brozopine (BZP) is an anti-ischemic drug. In this study, a model of VD in rats with modified permanent bilateral common carotid artery occlusion (2-VO) was established in vivo, a model of cellular excitotoxicity/oxidative stress was established via L-glutamate-induced PC12 cell injury, a model of neuroinflammation was established in LPS-induced BV2 cells in vitro, and the ameliorative effect of BZP on cognitive impairment was assessed. BZP treatment improved memory deficit in VD rats through inhibiting Ca2+overload and the levels of oxidative stress, ferroptosis, and inflammatory markers (IL-1β, IL-6, and COX-2) in different brain regions. Additionally, we found that the levels of inflammatory markers in the plasma were also reduced in the VD rats. BZP was further found to have antioxidative stress, antiferroptosis (ferroptosis markers: GPX4, P53, and ACSL4), and antineuroinflammatory effects in PC12 and BV2 cells. Its mechanisms of action were found to be related to the activation of the Nrf2/TLR4/NF-κB pathway; the protective effect of BZP was partially inhibited after using Nrf2-specific inhibitors. Thus, BZP has therapeutic properties for the potential mitigation of cognitive impairment.

In recent years, subspace clustering has become increasingly popular and achieved great success in band selection (BS) of hyperspectral imagery. However, current subspace clustering approaches are mostly insufficient in capturing the fine spatial structure and spectral correlation of image. Therefore, this article proposes a sample latent feature-associated low-rank subspace clustering model (SLFLRSC) tailored for BS. First, this model utilizes entropy rate segmentation to capture the rich spatial information of image. Meanwhile, Laplacian eigenmaps is employed to extract key latent features of samples in each region, enabling a joint representation of the original image that maximizes retention of key information while reducing noise and data dimensionality. Second, considering both short-range and long-range relationships of samples, a sample-spatial consistency constraint is formulated to reinforce the connections among similar samples across homogeneous and heterogeneous regions. Finally, a band-spectral local constraint is devised to rationally evaluate the global and local band adjacencies, incorporating both band similarity and spatial distance metrics. These initiatives provide a favorable condition for subspace clustering and BS tasks. The efficacy and reliability of SLFLRSC are confirmed through experiments on three datasets.

In recent years, sparse self-representation has achieved remarkable success in hyperspectral band selection. However, the traditional sparse self-representation-based band selection methods tend to neglect the spatial distribution differences and spectral redundancy between heterogeneous regions. Consequently, the uniform band subset obtained cannot accurately express the key features of various region-specific objects. In this context, this article proposes the structure-preserved and weakly redundant (SPWR) band selection method for hyperspectral imagery (HSI). Initially, to preserve the spatial structure of HSI, heterogeneous regions are generated by superpixel segmentation. This process simulates the actual distribution of ground objects and captures the spectral feature differences from heterogeneous regions, thus adapting the sparse self-representation to diverse land cover types. Subsequently, given that the different objects between heterogeneous regions have different sensitive bands, a series of region-specific multimetric hypergraphs are constructed to more accurately express the multivariate adjacencies between bands for each region. Significantly, a new spectral similarity measure that integrates both the spectral distance and physical distance is elaborately utilized to group bands into various hypergraphs. Finally, a consensus matrix is designed to fuse multiple coefficient matrices carrying the local spatial-spectral information of HSI, thereby selecting the subset of bands for a unified characterization of HSI and achieving the complementarity of multiple regions. Extensive comparison experiments on four real-world datasets demonstrate that the proposed method SPWR can efficiently select representative bands and outperforms other comparison methods.

The dynamic interplay between socioeconomic development and climate change has brought about significant changes in drought vulnerability within the African socioeconomic system. However, there is a prevailing lack of understanding about how these changes will manifest in an increasingly globalized economy under global warming. To address this knowledge gap, this study used various climatic, geographical, and socioeconomic data from historical periods and future projections to comprehensively assess the drought vulnerability in North and West Africa. Firstly, the drought vulnerability index (DVI) is calculated for every country to determine its accuracy and reliability based on the 26 elements and five components (i.e. health, energy and infrastructure, water resources, society, and economy). The datasets are collected for North and West African regions from the World Bank, AQUASTAT (global information system on water resources and agricultural water management), and the Food and Agricultural Organization (FAO) of the United Nations during 1980–2020. The calculated DVI is then compared to earlier observations of drought consequences. Then, using historical variations and trends, a regression models are applied to the historical DVI time-series for each country and projected for the years 2020–2100 to produce three forecast scenarios for DVI in the future (high, medium, and low). The findings indicate that in North and West Africa, Tunisia, Egypt, and Algeria are the regions least affected by drought with a DVI value between 0.2 and 0.4, while Niger, Chad, and Mauritania are the most vulnerable regions with a DVI value between 0.6 and 1. Future DVI forecasts show that the gap between high - and low -vulnerable countries will enlarge, with most northern African regions becoming less drought-vulnerable and most western African regions showing increased drought vulnerability. The results of this study are primarily helpful in performing risk assessments, developing response plans or making adaptation decisions to help communities consider possible ways to address anticipated current and future threats resulting from drought and to reduce vulnerability and enhance resilience of the system, coping with recurring drought in the coming decades.

Transparent phosphor ceramics have received increasing attention for high-brightness laser lighting, but commercially available phosphor ceramics are currently mainly limited to yellow YAG:Ce and green LuAG:Ce garnets, leaving a "cyan cavity" which is an obstacle to realizing full-color lighting. Achieving new phosphor ceramics capable of filling the cavity is a challenge. Herein, for the first time, cyan-green-emitting Ca3Sc2Si3O12:Ce3+ (CSS:Ce) transparent ceramics have been successfully developed by two-step sintering technique under vacuum. The as-prepared CSS:Ce ceramics present high relative density of 99.7% and optical transmittance of 71% in the cyan-green spectral region. It exhibits an efficient band emission peaking at 504 nm (under 450 nm excitation) with internal/external quantum efficiency of 91%/62%. Furthermore, it has excellent thermal stability with a thermal quenching temperature (T0.5) of 838 K, approximately 100 K higher than that of LuAG:Ce ceramics (738 K). In addition, the CSS:Ce ceramics can withstand blue laser density of 45.6 W/mm2 and meanwhile generates cyan-green light with a forward luminous flux of 813 lm and forward luminous efficacy of 162 lm/W. The CSS:Ce transparent ceramics exhibit excellent luminescence performance comparable to the commercial LuAG:Ce ceramics and could be a highly promising color converter for high-brightness laser lighting.

Synthesizing bare soil pictures in regions with complex vegetation is challenging, which hinders the accuracy of predicting soil organic carbon (SOC) in specific areas. An SOC prediction model was developed in this study by integrating the convolutional neural network and long and short-term memory network (CNN-LSTM) algorithms, taking into consideration soil-forming factors such as climate, vegetation, and topography in Hainan. Compared with common algorithmic models (random forest, CNN, LSTM), the SOC prediction model based on the CNN-LSTM algorithm achieved high accuracy (R2 = 0.69, RMSE = 6.06 g kg−1, RPIQ = 1.96). The model predicted that the SOC content ranged from 5.49 to 36.68 g kg−1, with Hainan in the central and southern parts of the region with high SOC values and the surrounding areas with low SOC values, and that the SOC was roughly distributed as follows: high in the mountainous areas and low in the flat areas. Among the four models, CNN-LSTM outperformed LSTM, CNN, and random forest models in terms of R2 accuracy by 11.3%, 23.2%, and 53.3%, respectively. The CNN-LSTM model demonstrates its applicability in predicting SOC content and shows great potential in complex areas where obtaining sample data is challenging and where SOC is influenced by multiple interacting factors. Furthermore, it shows significant potential for advancing the broader field of digital soil mapping.

With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and producing training samples, the limited training sample is a common problem that researchers often face. Furthermore, efficient algorithms are necessary to excavate the spatial and spectral information from these images, and then, make full use of this information with limited training samples. To solve this problem, a novel two-branch deep learning network model is proposed for extracting hyperspectral remote sensing features in this paper. In this model, one branch focuses on extracting spectral features using multi-scale convolution and a normalization-based attention module, while the other branch captures spatial features through small-scale dilation convolution and Euclidean Similarity Attention. Subsequently, pooling and layering techniques are employed to further extract abstract features after feature fusion. In the experiments conducted on two public datasets, namely, IP and UP, as well as our own labeled dataset, namely, YRE, the proposed DMAN achieves the best classification results, with overall accuracies of 96.74%, 97.4%, and 98.08%, respectively. Compared to the sub-optimal state-of-the-art methods, the overall accuracies are improved by 1.05, 0.42, and 0.51 percentage points, respectively. The advantage of this network structure is particularly evident in unbalanced sample environments. Additionally, we introduce a new strategy based on the RPNet, which utilizes a small number of principal components for feature classification after dimensionality reduction. The results demonstrate its effectiveness in uncovering compressed feature information, with an overall accuracy improvement of 0.68 percentage points. Consequently, our model helps mitigate the impact of data scarcity on model performance, thereby contributing positively to the advancement of hyperspectral remote sensing technology in practical applications.

The blast technology in metal mining is still the most important method of ore body mining. With the increase of mining depth, the blast process is more and more affected by the rock mechanical properties of the ore body, explosive properties, and crustal stress. In order to study the effects of rock physical and mechanical properties of the ore body, explosive detonation pressure and deep crustal stress on powder factor, blast action index and blast vibration. In this paper, a numerical simulation method under the fluid-solid coupling algorithm of rock body and explosive is constructed by combining the on-site blast crater test, Lsdyna JHC (Johnson Holmquist Concrete) damage model, and MATLAB parameter fitting algorithm. First, an exponential fitting function of the type “y=A×ex/B+C” is proposed to measure the rock body structure. Second, by modulating the strength and damage parameters and defining the unit failure criterion, the damage volume and blast action index obtained from the dynamic calculation are consistent with the 3D scanning results of the on-site blast crater test. Finally, the difference in HHT (Hilbert–Huang Transform) analysis results between the vibration signals of the surrounding rock body in simulation and test is clarified. By combining the blast crater test of the ore body and the numerical simulation of Lsdyna, the prediction of powder factor, blast action index, and blast vibration intensity is realized, which provides a reference for the controlled blasting of the deep ore body.

Abstract Despite increasing interest in developing ultrasensitive widefield diamond magnetometry for various applications, achieving high temporal resolution and sensitivity simultaneously remains a key challenge. This is largely due to the transfer and processing of massive amounts of data from the frame‐based sensor to capture the widefield fluorescence intensity of spin defects in diamonds. In this study, a neuromorphic vision sensor to encode the changes of fluorescence intensity into spikes in the optically detected magnetic resonance (ODMR) measurements is adopted, closely resembling the operation of the human vision system, which leads to highly compressed data volume and reduced latency. It also results in a vast dynamic range, high temporal resolution, and exceptional signal‐to‐background ratio. After a thorough theoretical evaluation, the experiment with an off‐the‐shelf event camera demonstrated a 13× improvement in temporal resolution with comparable precision of detecting ODMR resonance frequencies compared with the state‐of‐the‐art highly specialized frame‐based approach. It is successfully deploy this technology in monitoring dynamically modulated laser heating of gold nanoparticles coated on a diamond surface, a recognizably difficult task using existing approaches. The current development provides new insights for high‐precision and low‐latency widefield quantum sensing, with possibilities for integration with emerging memory devices to realize more intelligent quantum sensors.

Abstract The contactless temperature measurement at micro/nanoscale is vital to a broad range of fields in modern science and technology. The nitrogen‐vacancy (NV) center, a kind of diamond defect with unique spin‐dependent photoluminescence, is recognized as one of the most promising nanothermometers. However, this quantum thermometry technique is prone to a number of possible perturbations, which will unavoidably degrade its actual temperature sensitivity. Here, for the first time, a cross‐validated optical thermometry method is developed using a bulk diamond sample containing both NV centers and silicon‐vacancy (SiV) centers, achieving a sensitivity of 22 and 86 mK (√Hz)−1 respectively. Particularly, the latter has been intrinsically immune to those influencing perturbations for the NV‐based quantum thermometry, hence serving as a real‐time cross‐validation system. As a proof‐of‐concept demonstration, a trustworthy temperature measurement is shown under the influence of varying magnetic fields, which is a common artefact present in practical systems. This multi‐modality approach allows synchronized cross‐validation of the measured temperature, which is required for micro/nanoscale quantum thermometry in complicated environments such as a living cell.

Accurately estimating forest aboveground biomass (AGB) is imperative for comprehending carbon cycling, calculating carbon budgets, and formulating sustainable forest management plans. Currently, random forest (RF) and other machine learning models are widely used to estimate forest AGB, as they can effectively handle nonlinear relationships. However, by constructing a global model using all the samples collected from a study area, these models fail to account for the spatial heterogeneity in the AGB and cannot correct the prediction biases, thereby constraining the estimation accuracy. To overcome these limitations, we proposed a novel approach termed geographical random forest and empirical Bayesian kriging (GRFEBK). This hybrid model combines the localized modeling capability of geographical random forest (GRF) with the bias correction strength of empirical Bayesian kriging (EBK). GRF adapts RF to account for the spatial heterogeneity of the AGB, while EBK utilizes the spatial autocorrelation of residuals to correct the prediction deviations. This study was conducted in Hainan Island, utilizing spectral bands, vegetation indices, tasseled cap components derived from Landsat-8 imagery, backscattering coefficients from ALOS-2 synthetic aperture radar, topographic features, and the forest canopy height as the explanatory variables. A total of 195 forest aboveground biomass (AGB) samples were collected for modeling and assessing the predictive accuracy. The results demonstrate that, among the tested models, including GRFEBK, RF, support vector machine (SVM), k-nearest neighbor (KNN), geographically weighted regression (GWR), GRF, and EBK, GRFEBK attains the highest R2 (0.78) and the lowest RMSE (36.04 Mg/ha) and RRMSE (22.87%), significantly outperforming the conventional models and using GRF or EBK alone. These results demonstrate that by accounting for local non-stationarity in AGB and correcting prediction biases, GRFEBK achieves significantly higher accuracy than conventional RF and other models. While the results are promising, the computational cost of GRFEBK and its performance under varying geographical conditions warrant further investigation at larger scales to assess its broader applicability. Nevertheless, GRFEBK provides an innovative and more reliable approach for accurate forest AGB estimation with great potential to support global forest resource monitoring.

The Yellow River Delta wetlands in China belong to the coastal wetland ecosystem, which is one of the youngest and most characteristic wetlands in the world. The Yellow River Delta wetlands are constantly changed by inland sediment and the influence of waves and storm surges, so the accurate classification of the coastal wetlands in the Yellow River Delta is of great significance for the rational utilization, development and protection of wetland resources. In this study, the Yellow River Delta sentinel-2 multispectral data were processed by super-resolution synthesis, and the feature bands were optimized. The optimal feature-band combination scheme was screened using the OIF algorithm. A deep learning model attention mechanism ResNet based on feature optimization with attention mechanism integration into the ResNet network is proposed. Compared with the classical machine learning model, the AM_ResNet model can effectively improve the classification accuracy of the wetlands in the Yellow River Delta. The overall accuracy was 94.61% with a Kappa of 0.93, and they were improved by about 6.99% and 0.1, respectively, compared with the best-performing Random Forest Classification in machine learning. The results show that the method can effectively improve the classification accuracy of the wetlands in the Yellow River Delta.

The accurate monitoring of crop areas is essential for food security and agriculture, but accurately extracting multiple-crop distribution over large areas remains challenging. To solve the above issue, in this study, the Pixel-based One-dimensional convolutional neural network (PB-Conv1D) and Pixel-based Bi-directional Long Short-Term Memory (PB-BiLSTM) were proposed to identify multiple-crop cultivated areas using time-series NaE (a combination of NDVI and EVI) as input for generating a baseline classification. Two approaches, Snapshot and Stochastic weighted averaging (SWA), were used in the base-model to minimize the loss function and improve model accuracy. Using an ensemble algorithm consisting of five PB-Conv1D and seven PB-BiLSTM models, the temporal vegetation index information in the base-model was comprehensively exploited for multiple-crop classification and produced the Pixel-Based Conv1D and BiLSTM Ensemble model (PB-CB), and this was compared with the PB-Transformer model to validate the effectiveness of the proposed method. The multiple-crop cultivated area was extracted from 2005, 2010, 2015, and 2020 in North China by using the PB-Conv1D combine Snapshot (PB-CDST) and PB-CB models, which are a performance-optimized single model and an integrated model, respectively. The results showed that the mapping results of the multiple-crop cultivated area derived by PB-CDST (OA: 81.36%) and PB-BiLSTM combined with Snapshot (PB-BMST) (OA: 79.40%) showed exceptional accuracy compared to PB-Transformer combined with Snapshot and SWA (PB-TRSTSA) (OA: 77.91%). Meanwhile, the PB-CB (OA: 83.43%) had the most accuracy compared to the pixel-based single algorithm. The MODIS-derived PB-CB method accurately identified multiple-crop areas for wheat, corn, and rice, showing a strong correlation with statistical data, exceeding 0.7 at the municipal level and 0.6 at the county level.

Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits.

Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

Accurately predicting actual evapotranspiration (ETa) at the regional scale is crucial for efficient water resource allocation and management. While previous studies mainly focused on predicting site-scale ETa, in-depth studies on regional-scale ETa are relatively scarce. This study aims to address this issue by proposing a MulSA-ConvLSTM model, which combines the multi-headed self-attention module with the Pyramidally Attended Feature Extraction (PAFE) method. By extracting feature information and spatial dependencies in various dimensions and scales, the model utilizes remote sensing data from ERA5-Land and TerraClimate to attain regional-scale ETa prediction in Shandong, China. The MulSA-ConvLSTM model enhances the efficiency of capturing the trend of ETa successfully, and the prediction results are more accurate than those of the other contrast models. The Pearson’s correlation coefficient between observed and predicted values reaches 0.908. The study has demonstrated that MulSA-ConvLSTM yields superior performance in forecasting various ETa scenarios and is more responsive to climatic changes than other contrast models. By using a convolutional network feature extraction method, the PAFE method extracts global features via various convolutional kernels. The customized MulSAM module allows the model to concentrate on data from distinct subspaces, focusing on feature changes in multiple directions. The block-based training method is employed for the large-scale regional ETa prediction, proving to be effective in mitigating the constraints posed by limited hardware resources. This research provides a novel and effective method for accurately predicting regional-scale ETa.

Vegetation plays a vital role in the global carbon cycle, a function of particular significance in regulating carbon dioxide fluxes within tropical ecosystems. Therefore, it is crucial to enhance the precision of carbon dioxide flux estimates for tropical vegetation and to explore the determinants influencing carbon sequestration. In this study, Landsat series images and Sentinel-2 Multispectral Instrument satellite data were used to invert vegetation biophysical parameters, thereby improving the timeliness and resolution of state variables from the boreal ecosystem productivity simulator (BEPS). The BEPS model at a 30 m resolution was developed to accurately capture tropical vegetation carbon dioxide fluxes across Hainan Island (HN) over the preceding two decades. The impacts of climate variations and anthropogenic activities on the carbon dioxide fluxes of tropical vegetation were further quantified using quantile regression models and a land-use transfer matrix. Results indicate significant increases in both net primary productivity (NPP) and net ecosystem productivity (NEP) in HN during the period 2000–2020, by 5.81 and 4.29 g C/m2 year, respectively. Spatial trends in vegetation carbon dioxide fluxes exhibited a consistent decline from inland regions to coastal zones. Anthropogenic activities were the dominant factor in the reduced stability of coastal NPP, while the post-2005 vegetation restoration promoted the southward expansion of high NPP (>1200 g C/m2) in the central part of HN. NPP in this tropical island was more sensitive to temperature than to precipitation, with a 1 °C temperature increase resulting in 4.1 g C/m2 reduction in dry-season NPP compared to wet-season NPP. Upgrades of cropland quality and grassland restoration have improved NPP yields, and land use transfers have resulted in a 0.301 Tg C net increase in NPP. This study provides new insight into the improvement of the carbon dioxide flux model at a finer scale for tropical vegetation and highlights ecological construction as an adaptation strategy to enhance the carbon sinks of tropical vegetation under negative climate change conditions.

Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency (R2 = 0.85, RMSE = 2.2m, pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R2, RMSE, and pRMSE of 0.64, 3.38m, and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models.

In high-power impeller industries, valve-controlled liquid-filled hydrodynamic couplings are widely used in the soft startup of heavy-duty scraper conveyors for mining. However, the water circulation speed in internal flow fields is higher at lower speed ratios, making the hydrodynamic couplings prone to severe cavitation, which further results in severe performance degradation, noise, vibration, or even erosion failure. Meanwhile, because a hydrodynamic coupling is a piece of closed-loop multicomponent turbomachinery, internal transient cavitation flow behavior cannot be easily controlled. To reasonably predict the characteristics of cavitation and its influence on the working performance of the hydrodynamic coupling, a high-quality structured mesh model of the internal flow field for an impeller was established. Considering the periodic structural characteristics of the impeller, a scale-resolving simulation turbulence model was combined with a Rayleigh–Plesset cavitation model to establish a single-cycle hydrodynamic coupling calculation model. The cavitation distribution characteristics and torque transmission of the flow field under different working conditions were obtained, and the effect of cavitation on the soft startup performance was analyzed. The results demonstrated that cavitation in the hydrodynamic coupling mainly occurred under low speed ratios. The degree of cavitation decreased as speed ratio increased. The worst-case scenario for cavitation occurred when the speed ratio was zero. Most of the cavitation bubbles were generated at the tip of the blades, resulting in unstable variation in torque characteristics and deterioration of the working performance of the hydrodynamic coupling. The analysis reveals that the cavitation process in the impeller is highly unstable and periodic, and the cavitation development near the tip of the blades occurs in four stages: birth, growth, separation, and disintegration. The generated steam accumulates in the inner ring of the impeller. Therefore, a method for accurately predicting the cavitation characteristics of hydrodynamic couplings based on high-precision technology is proposed, and a theoretical basis for coupling design and cavitation suppression technology is provided.

Normalized Difference Vegetation Index (NDVI) is an effective proxy for vegetation activity and terrestrial productivity, and the widely used global NDVI products include the third generation Global Inventory Modelling and Mapping Studies Advanced Very High-Resolution Radiometer NDVI (NDVI3g), Satellite Pour l’Observation de la Terre VEGETATION NDVI (NDVIsp) and Terra Moderate Resolution Imaging Spectroradiometer NDVI (NDVItr). Here, for the first time, we investigated the differences between the three NDVIs in response and sensitivity to water stress represented by root zone soil moisture, vapor pressure deficit, self-calibrating Palmer Drought Severity Index and Standardized Precipitation Evapotranspiration Index across climate zones, humidity gradients, vegetation types and tree cover gradients. Over the temperate and arid climates region, the three NDVIs show higher consistent response fractions (CRFs) to water stress in the water-limited regions, with their sensitivity being in the order of NDVI3g

This study explores the cavitation suppression mechanisms of biomimetic hydrofoils inspired by whale flipper tubercles, focusing on viscous oil flow around hydrofoils. A novel test system for viscous oil cavitation was developed, featuring a high-speed camera to capture transient cavitation phenomena. The study compared the cavitation behaviour of the flow around a basic blade (Base-blade) with that around a biomimetic blade (Bio-blade) designed with leading-edge tubercles. The visualization results demonstrated that the biomimetic structure significantly reduced the degree and unsteadiness of cavitation. The study also employed a three-dimensional CFD model using the Stress-Blended Eddy Simulation (SBES) method and the Zwart-Gerber-Belamri (ZGB) cavitation mass transfer model to reveal the flow mechanism. The Bio-blade reduced the vapour volume fraction by 9.67%, decreased the drag coefficient (Cd) by 9.36%, and minimized the lift fluctuations compared to the Base-blade. The biomimetic design reduces the transient shedding cavitation scale, effectively suppressing severe cavitation events. The Bio-blade inhibited the formation of leading-edge separation vortices and reduced the scale of U-shaped vortices that enhance cavitation evolution. In summary, this study provides a comprehensive analysis of the cavitation suppression mechanisms of biomimetic hydrofoils in high-viscosity fluids, offering valuable insights for future research and engineering applications.

Abstract Background Current evidence demonstrates that blood glucose fluctuation can be associated with depression and anxiety. The association among blood glucose fluctuation, traditional risk factors and emotional disorders in T2DM should be studied and clarified. Methods A total of 182 diabetic patients including 81 patients with depression or anxiety and 101 patients without emotional disorder were enrolled into this study. Data were obtained through medical history and questionnaire survey. Data were analyzed using appropriate statistical methods. Results The comparison results of basic information between the two groups showed that the differences of the proportion of female were statistically significant (p = 0.002). There was no statistical difference in laboratory examination indexes between the two groups, however, standard deviation of blood glucose (SDBG) and postprandial glucose excursion (PPGE) of the comorbidity group were significantly higher than that of control group (p = 0.032 and p = 0.037). The results of questionnaire survey showed that there were statistically significant differences in sleep quality, PSQI and dietary habit between the two groups (p

Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be negligible, we established a machine learning model (i.e., extreme gradient boosting algorithm (XGBoost)) for soil evaporation estimation based on night-time evapotranspiration observation data from eddy covariance towers, remote sensing data, and meteorological reanalysis data. Daytime T was consequently calculated as the difference between the total evapotranspiration and predicted daytime soil evaporation. The soil evaporation estimation model was validated based on the remaining night-time ET data (i.e., model test dataset), the non-growing season ET data of the natural ecosystem, and ET data during the fallow periods of croplands. The validation results showed that XGBoost had a better performance in E estimation, with the average overall accuracy of NSE 0.657, R 0.806, and RMSE 11.344 W/m2. The average annual T/ET of the examined ten ecosystems was 0.50 ± 0.08, with the highest value in deciduous broadleaf forests (0.68 ± 0.11), followed by mixed forests (0.61 ± 0.04), and the lowest in croplands (0.40 ± 0.08). We further examined the impact of the leaf area index (LAI) and vapor pressure deficit (VPD) on the variation in T/ET. Overall, at the interannual scale, LAI contributed 28% to the T/ET variation, while VPD had a small (5%) influence. On a seasonal scale, LAI also exerted a stronger impact (1~90%) on T/ET compared to VPD (1~77%). Our study suggests that the XGBoost machine learning model has good performance in ET partitioning, and this method is mainly data-driven without prior knowledge, which may provide a simple and valuable method in global ET partitioning and T/ET estimation.

In agricultural and fishery production, the occurrence of cyanobacterial blooms and the contamination of freshwater systems with microplastics have become increasingly important research focuses. However, the individual and combined toxic mechanisms of these two pollutants are not yet fully understood. Therefore, in this study, we analyzed the effects of these two stressors, microcystins (MC) and nanoplastics (NP), on the transcriptome and metabolome of the hepatopancreas of river clams. RNA and metabolites extracted from river clams treated with MC, NP, and a combination of MC and NP were used to construct standardized cDNA libraries, which were then subjected to integrated analysis. Significant enrichment of 49 pathways, 34 pathways, and 44 pathways was observed in the MC group compared to the control group, NP group compared to the control group, and NP-MC group compared to the control group, respectively. In these three experimental groups, we found that the lysosome pathway, which affects immune function and cell apoptosis, was enriched with numerous differentially expressed genes and metabolites. Changes in ATP6N and ADP may impair lysosomal acidification and disrupt normal lysosomal degradation processes, indicating interference with the hepatopancreatic metabolism of pollutants. Interestingly, we observed significant alterations in the cathepsin family, and the downregulation of cathepsin genes, along with the downregulation of ATP6N, implies a potential disruption in lysosomal proteolysis. In the NP-MC group, the downregulation of purine expression levels suggests an impact on the immune system of river clams by NP-MC. In conclusion, while there is some overlap in the damage caused to the hepatopancreas of river clams by MCs, MPs, and the combination of NP-MC, further research is necessary to fully understand their effects.

Remote sensing techniques for shoreline extraction are crucial for monitoring changes in erosion rates, surface hydrology, and ecosystem structure. In recent years, Convolutional neural networks (CNNs) have developed as a cutting-edge deep learning technique that has been extensively used in shoreline extraction from remote sensing images, owing to their exceptional feature extraction capabilities. They are progressively replacing traditional methods in this field. However, most CNN models only focus on the features in local receptive fields, and overlook the consideration of global contextual information, which will hamper the model’s ability to perform a precise segmentation of boundaries and small objects, consequently leading to unsatisfactory segmentation results. To solve this problem, we propose a parallel semantic segmentation network (TCU-Net) combining CNN and Transformer, to extract shorelines from multispectral remote sensing images, and improve the extraction accuracy. Firstly, TCU-Net imports the Pyramid Vision Transformer V2 (PVT V2) network and ResNet, which serve as backbones for the Transformer branch and CNN branch, respectively, forming a parallel dual-encoder structure for the extraction of both global and local features. Furthermore, a feature interaction module is designed to achieve information exchange, and complementary advantages of features, between the two branches. Secondly, for the decoder part, we propose a cross-scale multi-source feature fusion module to replace the original UNet decoder block, to aggregate multi-scale semantic features more effectively. In addition, a sea–land segmentation dataset covering the Yellow Sea region (GF Dataset) is constructed through the processing of three scenes from Gaofen-6 remote sensing images. We perform a comprehensive experiment with the GF dataset to compare the proposed method with mainstream semantic segmentation models, and the results demonstrate that TCU-Net outperforms the competing models in all three evaluation indices: the PA (pixel accuracy), F1-score, and MIoU (mean intersection over union), while requiring significantly fewer parameters and computational resources compared to other models. These results indicate that the TCU-Net model proposed in this article can extract the shoreline from remote sensing images more effectively, with a shorter time, and lower computational overhead.

Accurate prediction of future chlorophyll-a (Chl-a) concentrations is of great importance for effective management and early warning of marine ecological systems. However, previous studies primarily focused on chlorophyll-a inversion and reconstruction, while methods for predicting Chl-a concentrations remain limited. To address this issue, we adopted four deep learning approaches, including Convolutional LSTM Network (ConvLSTM), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Eidetic 3D LSTM (E3D-LSTM), and Self-Attention ConvLSTM (SA-ConvLSTM) models, to predict Chl-a over the Yellow Sea and Bohai Sea (YBS) in China. Furthermore, 14 environmental variables obtained from the remote sensing data of Moderate-resolution Imaging Spectroradiometer (MODIS) and ECMWF Reanalysis v5 (ERA5) were utilized to predict the Chl-a concentrations in the study area. The results showed that all four models performed satisfactorily in predicting Chl-a concentrations in the YBS, with SA-ConvLSTM exhibiting a closer approximation to true values. Furthermore, we analyzed the impact of the Self-Attention Memory Module (SAM) on the prediction results. Compared to the ConvLSTM model, the SA-ConvLSTM model integrated with the SAM module better captured subtle large-scale variations within the study area. The SA-ConvLSTM model exhibited the highest prediction accuracy, and the one-month Pearson correlation coefficient reached 0.887. Our study provides an available approach for anticipating Chl-a concentrations over a large area of sea.

Abstract A principle of choice in animal decision-making named probability matching (PM) has long been detected in animals, and can arise from different decision-making strategies. Little is known about how environmental stochasticity may influence the switching time of these different decision-making strategies. Here we address this problem using a combination of behavioral and theoretical approaches, and show, that although a simple Win-Stay-Loss-Shift (WSLS) strategy can generate PM in binary-choice tasks theoretically, budgerigars (Melopsittacus undulates) actually apply a range of sub-tactics more often when they are expected to make more accurate decisions. Surprisingly, budgerigars did not get more rewards than would be predicted when adopting a WSLS strategy, and their decisions also exhibited PM. Instead, budgerigars followed a learning strategy based on reward history, which potentially benefits individuals indirectly from paying lower switching costs. Furthermore, our data suggest that more stochastic environments may promote reward learning through significantly less switching. We suggest that switching costs driven by the stochasticity of an environmental niche can potentially represent an important selection pressure associated with decision-making that may play a key role in driving the evolution of complex cognition in animals.

Sea fog is a weather hazard along the coast and over the ocean that seriously threatens maritime activities. In the deep learning approach, it is difficult for convolutional neural networks (CNNs) to fully consider global context information in sea fog research due to their own limitations, and the recognition of sea fog edges is relatively vague. To solve the above problems, this paper puts forward an ECA-TransUnet model for daytime sea fog recognition, which consists of a combination of a CNN and a transformer. By designing a two-branch feed-forward network (FFN) module and introducing an efficient channel attention (ECA) module, the model can effectively take into account long-range pixel interactions and feature channel information to capture the global contextual information of sea fog data. Meanwhile, to solve the problem of insufficient existing sea fog detection datasets, we investigated sea fog events occurring in the Yellow Sea and Bohai Sea and their territorial waters, extracted remote sensing images from Moderate Resolution Imaging Spectroradiometer (MODIS) data at corresponding times, and combined data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), cloud and sea fog texture features, and waveband feature information to produce a manually annotated sea fog dataset. Our experiments showed that the proposed model achieves 94.5% accuracy and an 85.8% F1 score. Compared with the existing models relying only on CNNs such as UNet, FCN8s, and DeeplabV3+, it achieves state-of-the-art performance in sea fog recognition.

Understanding variations in the temporal and spatial distribution of vegetation phenology is essential for adapting to and mitigating future climate change and urbanization. However, there have been limited vegetation phenology studies within small-scale areas such as urban environments over the past decades. Therefore, the present study focuses on Jinan city, Shandong Province, China as the study area and employs a more refined local climate zone (LCZ) approach to investigate spatial and temporal variations in vegetation phenology. The three phenological indicators used in this study from 2007 to 2018, namely, the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS), were provided by MODIS satellite data. The SOS, EOS, and LOS were superimposed on the LCZ and urban–rural gradient to analyze the changes in vegetation phenology, and the applicability of these two analysis methods in the study of urban vegetation phenology was compared by the honest significant difference test. We found that the SOS, EOS, and LOS of vegetation in the study area generally showed an advance, delay, and extension trend, respectively. The means of the SOS and EOS along different LCZ types varied noticeably more than those along urban–rural gradients. In 2016, 77.5%, 80.0%, and 75.8% of LCZ pairs indicated statistically significant differences for SOS, EOS, and LOS, respectively. This study provides a new perspective for the study of urban vegetation phenology which can help in management of urban-scale environments, identification of areas rich in biodiversity, and conservation and restoration of biodiversity in urban areas.

Accurately estimating aboveground biomass (AGB) is essential for assessing the ecological functions of coastal wetlands, and AGB of coastal wetlands is affected by Land use/land cover (LULC) types of conversion. To address this issue, in the current study, we used the Boreal Ecosystem Productivity Simulator (BEPS) model to simulate the AGB of the Yellow River Delta during 2000–2015. Based on the LULC types transform, we analyzed the spatiotemporal dynamics of the AGB simulation results and their relationship with the human-nature driving process. At the same time, combined with the actual situation of LULC transformation in the Yellow River Delta, a new driving process (Replace) is introduced. The results show that from 2000 to 2015, 755 km2 of natural wetlands in the Yellow River Delta were converted into constructed wetlands, and AGB increased by 386,121 Mg. Both single and multiple driving processes contributed to the decrease in AGB, with 72.6% of the increase in AGB associated with single artificial (such as Restore) or natural (such as Accretion) driving processes and 27.4% of the increase in AGB associated with multiple driving processes. Naturally driven processes bring much more AGB gain than loss, and human-driven processes bring the largest AGB gain. LULC conversion brought on by anthropogenic and natural driving processes has a large impact on AGB in coastal wetlands, and exploring this impact has a significant role in planning coastal wetland land use and protecting blue carbon ecosystems.

Extra care should be taken when new tunnels pass through an existing tunnel. If it is not handled properly, this will affect the operation safety of the existing line, and bring security risks to the train’s operation. In order to study the impact of an under-crossing parallel double-line shield tunnel on the existing shield tunnel structure, the influence of tunnel construction on the deformation of overlying strata was analyzed, and the formula for estimating the formation settlement at depth Z below the surface, caused by the excavation of a double-tunnel parallel tunnel, was deduced. Then, a series of three-dimensional finite element numerical simulations were carried out. We analyzed and systematically studied the adverse effects of the tunnel structure of Guangzhou subway Line 5, caused by the tunneling of subway Line 18, evaluated its structure and operational safety, and provided suggestions for site construction. This research demonstrates the following conclusions. (1) The tunnel structures of subway Line 5 and Line 18 are mainly in the strongly weathered argillaceous siltstone stratum, and lightly weathered argillaceous siltstone stratum, respectively, and the stratum where the Line 18 tunnel is located is relatively safe. (2) According to three-dimensional finite element numerical simulation analysis, during the shield-tunneling process of subway Line 18, the maximum X horizontal displacement, the maximum Y horizontal displacement, and the maximum Z vertical displacement of the tunnel structure in subway Line 5 are 1.09, 3.50, and 4.55 mm, respectively. It is considered that the impact of the shield-tunnel penetration of subway Line 18 on the tunnel structure of subway Line 5 is relatively controllable, and does not affect the structure and operational safety. (3) It is suggested that settlement monitoring should be strengthened within the range of 12 m (about 1.5 tunnel diameter D). before and after the excavation axis of the underpass tunnel, and it is necessary to carry out local reinforcement treatment, to prevent adverse effects on the operation of the existing tunnel.

A nanofluidic energy absorption system (NEAS) is composed of nanoporous material and functional liquid with high energy absorption density. Applying an electric field to adjust the energy absorption characteristics of a nanofluidic system will open broader prospects for its application. In the current work, ZSM-5 zeolite was adopted as the nanoporous material and water, a 25% KCl solution, and a saturated KCl solution were adopted as functional liquids to configure NEASs. Pressure-induced infiltration experiments were carried out to study the infiltration and defiltration characteristics of the NEASs under the action of an applied electric field. The results show that the introduction of an applied electric field can weaken the hydrogen bonds between molecules, thus reducing the equivalent surface tension and contact angle, changing the infiltrability of liquid molecules into the nanopores, and reducing the infiltration pressure of the system. In an electrolyte solution/zeolite system, the anions and cations move close to the two plate electrodes under the action of an external electric field, and the fluid properties in the central zone of the pressure chamber are close to the water/zeolite system. For both an ultra-low conductivity liquid and an electrolyte solution/zeolite system, applying an electric field can effectively improve the relative outflow rate of liquid, thus improving the reusability of the system.

Ultraviolet‐C illumination may provide a promising solution for the disinfection technology, and thus the development of novel type of ultraviolet‐C light sources is continuously expected. Herein, a conceptual design of ultraviolet‐C light source based on up‐conversion luminescence of phosphor material is introduced. Accordingly, a composite film is prepared using YBO3:Pr3+ phosphor and a transmissive remote phosphor‐converted light source is built by combing the phosphor film with a 447 nm laser. Spectral measurements show that the phosphor‐converted design emits ultraviolet‐C light peaking at 263 and 274 nm with tunable emission intensities. Moreover, ultraviolet imaging demonstration implies that the present design has a potential to serve as an ultraviolet‐C germicidal light source for surface decontamination.

Accurate information on cropland evapotranspiration (ET) can facilitate effective agricultural management. However, the application of existing physical models over broad regions may be impeded due to the need for difficult to acquire information about environmental factors that constrain ET. The recently developed near-infrared reflectance of vegetation (NIRv), which can reasonably characterize ecosystem photosynthesis without the need for additional environmental information, is potentially useful for estimating cropland ET and reducing the difficulty in ET modeling. As such, we proposed two simply formulated semi-empirical models that utilize NIRv as a major factor constraining cropland ET. The first model, termed Penman–Monteith+ (PM+), computed canopy transpiration using the PM equation along with canopy conductance values estimated from NIRv-derived gross primary productivity (NIRv-GPP) and calculated soil evaporation using an empirical approach. Another model, termed underlying water-use efficiency+ (uWUE+), used the uWUE approach along with the NIRv-GPP to predict ET. We calibrated and validated PM+ and uWUE+ over 32 cropland flux sites and then compared them with six complex models. The better model between PM+ and uWUE+ was applied to estimate regional ET over North China Plain (NCP), where typical C3 and C4 crops were planted during 2010–2018, along with remote sensing and meteorological data. Results indicated that the two new models can reasonably estimate cropland ET. The PM+ model reproduced an eight-day value of ET (denoted as eight-day ET) with R2 = 0.741 and RMSE = 5.638 mm/8d, slightly better than the uWUE+ model (R2 = 0. 674 and RMSE = 6.275 mm/8d) for a cross-site validation over 17 flux sites of the validation dataset. Site-level validation revealed consistently better performance of PM+ compared to uWUE+ over most flux sites. For the comparisons with six existing models, PM+ can perform better than all these models and uWUE+ is better than five of them. Subsequently, the PM+ (uWUE+) model reasonably reproduced eight-day ET over four sites under a dry climate (Arid Index ≤ 0.5) with site-level R2 = 0.691 (0.617) and RMSE = 6.663 (7.906) mm/8d on average, which is better than the six existing models. In addition, the regional mean annual ET varied from 590 mm per year (mm/yr) to 680 mm/yr over NCP with a significant increasing trend of 9.05 mm/yr (p

Gestational diabetes mellitus (GDM) commonly leads to adverse pregnancy outcomes and long-term metabolic complications in offspring. Breastfeeding has been shown to rewrite the fetal “metabolic programming” resulting from maternal diabetes and finally lead to a lower risk of future metabolic disease. Lipids in breast milk act like hormones to promote infant growth and development, but there is minimal information invested thus far in constitution changes of lipids in breast milk, especially in the context of GDM. In the present study, we performed a lipidomics analysis to compare the lipid composition in breast milk collected from women with or without GDM. We further revealed the correlations of dysregulated lipids in breast milk with maternal glucose and infant physical development. A total of 833 lipid species from 15 classes were identified, 60 of which were found to be significantly altered in response to the high glucose, suggesting a remarkable lipid profiling change in breast milk induced by GDM. Our results showed significant associations between dysregulated lipids (e.g., neutral lipids, phospholipids, sphingolipids) and maternal glucose. Furthermore, correction analysis demonstrated that GDM related lipids were also associated with indicators of infant physical development, including body weight, length, and head circumference. These findings may help to understand the protective effects of breastfeeding especially during GDM pregnancy.

Abstract Understanding present and future impacts of drought on the terrestrial carbon budget is of great significance to the evaluation of terrestrial ecosystem disturbance and terrestrial carbon sink. Here, we evaluate the effect of vegetation net primary productivity (NPP) associated with drought through the difference between the mean NPP in the drought and normal years during a specific time period (30 years). Then, the NPP effects in different vegetation types and climatic zones under baseline stage (1981–2010) and future climate scenarios (RCP2.6, RCP4.5, and RCP8.5) is assessed. The results indicate that the negative NPP extremes are captured in most regions, except for the high‐latitude in the Northern Hemisphere. The NPP loss caused by extreme droughts in 2071–2100 is largest under RCPs, followed by the effects of severe and moderate droughts. Regionally, central United States, southern Africa, central Asia, India, Amazon tropical rainforest, and Australia are projected to experience a significant increase in negative NPP extremes and most of these regions are in arid and semi‐arid and tropical rain forest areas. In contrast, tropical Asia suffers little drought effects. For different vegetation, Evergreen Broadleaf Forest, Closed Shrubland, Open Shrubland, Croplands, and Grassland are the most affected by drought. The largest NPP loss occurs in most part of regions under RCP4.5 scenario, not RCP8.5. Climate change is projected to play the largest role in aggravating the risk of drought‐induced NPP reduction. And meanwhile, the adverse effects of drought on vegetation may be resisted through rational fertilizer utilization and land management in future.

Background: Oxidative stress is associated with the pathogenesis of ischemic stroke (±)-5-bromo-2-(5-fluoro-1-hydroxyamyl) benzoate (BFB) is a novel compound modified by dl-3-n-butylphthalide (NBP). Here, we hypothesized that BFB may protect the PC12 cells against H2O2-induced oxidative stress injury through activation of the Nrf2 pathway.Methods: We measured the cell viability and levels of lactate dehydrogenase (LDH), malondialdehyde (MDA), glutathione (GSH), and reactive oxygen species (ROS) to determine the construction of the H2O2-induced models of oxidative stress in PC12 cells. Additionally, apoptotic cell death, mitochondrial membrane potential, and cellular morphology were examined to determine the effect of BFB on oxidative stress injury in H2O2-treated PC12 cells. The expression levels of Nrf2-related and autophagy-related genes and proteins were detected using real time quantative PCR (RT-qPCR), Western Blot, and immunofluorescence analyses.Results: Our study showed that BFB treatment reduced the elevated levels of MDA, LDH, and ROS, and decreased cell viability and GSH in H2O2-treated PC12 cells. We also observed the elevated expression of Nrf2 pathway-related factors and intranuclear transitions and found that Nrf2 inhibitors (ML385) could block the protective effect of BFB. The inhibitory effect of BFB on oxidative stress may be partially regulated by Nrf2 activation, and the initiation and induction of autophagy.Conclusion: BFB inhibited H2O2-induced oxidative stress injury in PC12 cells by activating the Nrf2 pathway, initiating and inducing autophagy, suggesting that BFB may be a promising therapeutic agent in treating neurological disorders like cerebral ischemia.

Study region: East Africa (EA), lake basins (Tana, Abaya-Chamo, Turkana, Victoria, and Tanganyika), 6472,404 km2 of total area. Study focus: Total water storage change (TWSC) exposes serious challenges in sustainable water resources management and ecosystem sustenance. While TWS changes have been increasingly studied based on Gravity Recovery and Climate Experiment (GRACE) derived TWS dataset, spatio-temporal changes in TWS and the driving factors remain unexplored. Herein, Mann–Kendall test, Theil-Sen's slope estimator, empirical orthogonal functions (EOFs), component contribution ratio (CCR), and Soil and Water Assessment Tool (SWAT) models were employed. We found a slight spatial fluctuation of TWS, and groundwater could affect TWS variability in the EA region. New hydrological insights: High annual TWSC was observed in the southwestern and northern regions, reaching values of 220 and − 100 mm/yr, respectively. Among lake basins, the maximum and minimum TWSC values attained 97.5 ± 28.2 mm/yr and 24.8 ± 7.7 mm/yr, in Victoria and Tana basins, respectively. The regional level groundwater contribution was about 42%, while 48%, 53%, and 46% were in Abaya-Chamo, Turkana, and Victoria basins, respectively. However, surface runoff contribution was 52% and 35% in Tana and Tanganyika basins, respectively. The seasonal TWS changes show substantial linkage with the lake basins' seasonal precipitation distribution. Our findings highlight the driving factors of TWS variability, which could help guiding water management policies, and promote satellite remote sensing datasets.

The purpose of this paper is to study the mechanism and improve the prediction accuracy of transient torque converter cavitation flow by the application of scale-resolving simulation (SRS) methods with particular focus on cavitation vortex flow. Firstly, the numerical analysis of the entire internal flow field of the torque converter was carried out using different turbulence models, and the prediction accuracy of the hydraulic characteristics of the adopted models was analyzed and validated via test data. Secondly, the cavitation and turbulence behavior in the internal flow field were analyzed, and the blade surface pressure according to different turbulence models was compared and validated through test data. Finally, the transient cavitation characteristics of the flow field were studied based on the stress-blended eddy simulation (SBES) model. The prediction accuracy of the cavitation flow field simulation of the torque converter is significantly improved using the SRS model. The maximum error of capacity constant, torque ratio and efficiency are reduced to 3.1%, 2.3%, and 1.3% at stall, respectively. The stator is more prone to cavitation than pump and turbine. The SBES model has the highest prediction accuracy in multiple measurement points, and the maximum deviation can reach 13.32% under stall. Attached cavitation bubbles and periodic shedding cavitation can be found in the stator, and the evolution period is about 0.0036 s, i.e., 279 Hz. The prediction accuracy of different models was compared and analyzed, which has important guiding significance for the high-precision prediction and analysis of fluid machinery.

The rapid intensification of drought, commonly known as flash drought, has recently drawn widespread attention from researchers. However, how the characteristics and drivers, as well as the ecological impacts of rapidly intensified droughts, differ from those of slowly intensified ones still remains unclear over the globe. To this end, we defined three types of droughts based on the root zone soil moisture (RZSM) decline rates, flash droughts, general droughts, and creep droughts, and then implemented a comparative analysis between them across the globe and the 26 Intergovernmental Panel on Climate Change Special Report on Extremes (IPCC-SREX) regions. The ensemble of RZSM from multiple reanalysis datasets was used to reduce the uncertainties. According to the frequency analysis, our findings suggest that flash droughts contributed to the majority of drought events during 1980–2019, indicating the prevalence of rapid transition from an energy-limited to a water-limited condition in most of the regions. The comparative results of vegetation responses show that flash droughts are more likely to happen in the growing season, leading to faster but relatively minor vegetation deterioration compared to the slowly intensified ones. By analyzing the precipitation and temperature anomalies in the month of drought onset, we found the role of temperature (precipitation) on drought intensification can be generalized as the warmer (drier) the climate is or the faster the drought intensifies, but the main driving forces vary by region. Unlike temperature dominating in midwestern Eurasia and northern high latitudes, precipitation plays a prominent role in the monsoon regions. However, the temperature is expected to be the decisive driver in the warming future, given its monotonically increased contribution over the past four decades.

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models.

Virtual Reality (VR) shows the potential to tackle the limitations and deficiencies of traditional microteaching training by offering an intricate and lifelike environment that helps pre-service teachers to develop their teaching skills. However, the effects of VR training on each teaching skill sub-dimension remain inconclusive. This study identified nine sub-dimensions of teaching skills through a literature review. Moreover, a VR-based microteaching training system was developed, and the effects of VR on these sub-dimensions of teaching skills were explored by conducting a comparative experiment between virtual and traditional microteaching training. The results showed that virtual microteaching considerably enhanced pre-service teachers' teacher efficacy compared to traditional training, especially the efficacy of instructional strategies and classroom management. Furthermore, more significant improvements were noted in the questioning skill and the language and posture skills after virtual microteaching training, although there was no significant difference in teaching skills overall. Moreover, pre-service teachers reported a highly immersive experience during the virtual microteaching.

Orthorhombic ZrTiO4 is an attractive dielectric material; its optical properties are, however, less known. In this paper, we reported on the microstructure and luminescence studies of pristine ZrTiO4 and Eu3+-doped ZrTiO4 phosphors. The results indicated that two types of TiO6 octahedra, the isolated/ localized and coupled/delocalized, coexisted in host matrix. Eu3+ doping could induce oxygen vacancy defect states located below the bottom of the conduction band. Pristine ZrTiO4 showed bright yellow luminescence via STEs recombination at defects sites at low temperatures, but significant thermal quenching occurred due to STEs migration to quenching centers at elevated temperatures. Effective host sensitized energy transfer to Eu3+ was observed in ZrTiO4:Eu3+ phosphors and yielded the red characteristic emissions of Eu3+. Anomalous STEs luminescence enhancement and spectral blue-shift in the excitation spectra with higher Eu3 + concentration appeared and were explained by considering three factors: competitive absorption between electron transitions from the top of the valence band to the defect states and host conduction band, Eu3+ doping driving the production of more isolated TiO6 octahedra, and energy back-transfer from Eu3+ activators to other titanate groups. On the basis of the dual-emitting combination strategy involving host STEs and Eu3+ luminescence, ZrTiO4:Eu3+ phosphors were demonstrated to be ratiometric self-referencing optical thermometric materials, with a working range of 153–313 K and a maxima of relative sensitivity to ~1.1% K−1 at 243 K.

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring.

Evapotranspiration (ET) is a pivotal process for ecosystem water budgets and accounts for a substantial portion of the global energy balance. In this paper, the exited actual ET main datasets in global scale, and the global ET modeling and estimates were focused on discussion. The Source energy balance (SEB) models, empirical models and other process-based models are summarized. Accuracy for ET estimates by SEB models highly depends on accurate surface temperature retrieval, and SEB models are hard to apply in large heterogeneous surface. The Penman–Monteith (PM) equations are thought to be with considerable sound mechanism. However, it involves large number of parameters, which are not all global available. A simplified PM equation by Priestley and Taylor (PT) is found to perform well on well-watered surface. For both PM and PT equations in estimating ET, the key is to consider the constraint from surface resistance primarily water stress. Empirical models are simple but the accuracy of which highly depends on training samples. Coupling satellite data into ET models can improve ET estimates with higher resolution spatiotemporal information inputs; However, finding the most proper way to estimate global ET remains problematic. Several reasons for this issue are also analyzed in this review.

This study explored the applicability of Temperature Vegetation Dryness Index (TVDI) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index (NDVI) and land surface temperature (LST) data for drought monitoring in Eurasia from April to September during 2005 - 2014. TVDI was calculated based on the vegetation index/temperature trapezoid eigenspace (VITT) in this study. The Standardized Precipitation Evapotranspiration Index in three time scales (SPEI-01, SPEI-03, SPEI-06) and the Essential Climate Variable surface soil moisture product (ECV-SM) was used to evaluate the performance of TVDI over Eurasia, and to assess the sensitivity of TVDI to different land-cover types. The records of drought events from Emergency Events Database (EM-DAT) were also used to validate TVDI. The results indicated that TVDI negatively responded to SPEIs about 77.43%, 72.01%, and 75.95% of lands respectively, of which 29.84%, 33.93%, and 25.59% were subjected significant relationships (p 0.86. A comparison of TVDI with drought records demonstrated that the TVDI could capture drought events in study area.

Drought is a very complex natural hazard and has a negative impact on the global ecosystem as a whole. Recently Bangladesh has been experiencing by different degree of dryness as a consequence of high climate variability, affecting the crop production to a great extent in the last couple of decades. In this context, the present study was made an effort to assess and analyse drought characteristics based on two drought indices, i.e., Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI), and model agricultural drought risk with Fast-and-frugal decision tree (FFT) model in Bangladesh from 2001 to 2016. We identified drought occurrence and its dynamics with three-time scale, i.e., SPI3J (November-January), SPI3A (February-April) and SPI6A (November-April), and three rice-growing seasons, i.e., Aus (March-July), Aman (June-November), and Boro (November-May) from TRMM (Tropical Rainfall Measuring Mission) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. The results demonstrate that TRMM had good consistency with rain gauge measurement compared to CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data to derive SPI3J, SPI3A and SPI6A. Overall results confirmed that more drought frequency observed in SPI6A than SPI3J and SPI3A time scale, representing moderate to severe drought throughout the country. Regarding agricultural drought resulting from VCI demonstrated Boro rice-growing season as more vulnerable crop growing season affected by severe to extreme drought event. Validation results of VCI exhibited a high correlation with rice yield data than in-situ soil moisture data. Results of the FFT model show that out of ten predictor variables SPI3J and SPI6A caused agricultural drought with SPI value less than -1.08 and -1.21 respectively. Additionally, the model characterized SPI3J and SPI6A as the most critical driving factors with the highest balanced accuracy triggering agricultural drought risk in Bangladesh.

Evaluating the potential productivity of the terrestrial ecosystem is extremely important to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, carbon sequestration and to mitigate climate warming caused by rising CO2 concentrations. However, most previous studies neglected the optimum state of natural vegetation without human intervention and regional change trend of vegetation under future climate change. In this study, variations in spatio-temporal distributions of climate potential productivity (CPP) over China from 1980 to 2018 are analyzed with the synthetic estimating model. A comprehensive regionalization method (Principal components analysis, PCA) based on standardized precipitation evapotranspiration index (SPEI), and statistical analysis methods are adopted to assess CPP and its response to the climate change in different regions of China. The results demonstrate that the global temperature rising and precipitation decreasing have obvious effects on the productivity of terrestrial ecosystem and its spatio-temporal distribution in different sub-regions and ecosystems. Among them, precipitation is the dominant factor, and temperature significantly affects some regions such as Tibetan Plateau (TP) and Northeast China (NE) with high-altitude or high-latitude. The optimum temperature for the CPP in Xinjiang (XJ) region and Northwest China (NW) is 7.5°C and 8°C, respectively. With regards to the ecosystems, the CPP of grassland shows complex trends in XJ, Southwest China (SW), NE, and TP; especially in XJ (NE), the CPP shows a decreasing (an increasing) trend when the temperature is more than 7.5°C (0°C). Linear correlations occur between farmland CPP and temperature in each sub-region except for XJ. The same situation also exists at forest CPP, especially in TP, NE and NC regions. However, under the temperature increasing and precipitation decreasing, there are slight adverse impacts on the CPP of vegetation at the national scale, indicating that drier and warmer climate are detrimental for vegetation growth.