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Ultraviolet (UV) radiation is closely related to health; however, limited measurements have hindered further investigation of its health effects in China. Machine learning algorithms have been widely used to predict environmental factors with high accuracy, but a limited number of studies have implemented it for UV radiation. The main aim of this study is to develop a UV radiation prediction model using the random forest approach and predict the UV radiation with a daily and 10 km resolution in mainland China from 2005 to 2020. The model was developed with multiple predictors, such as UV radiation data from satellites as independent variables and ground UV radiation measurements from monitoring stations as the dependent variable. Missing satellite-based UV radiation data were obtained using the 3 d moving average method. The model performance was evaluated using multiple cross-validation (CV) methods. The overall R2 and root mean square error between measured and predicted UV radiation from model development and model 10-fold CV were 0.97 and 15.64 W m−2 and 0.83 and 37.44 W m−2 at the daily level, respectively. The model that incorporated erythemal daily dose (EDD) retrieved from the Ozone Monitoring Instrument (OMI) had a higher prediction accuracy than that without it. Based on predictions of UV radiation at the daily level, 10 km spatial resolution, and nearly 100 % spatiotemporal coverage, we found that UV radiation increased by 4.20 %, PM2.5 levels decreased by 48.51 %, and O3 levels increased by 22.70 % from 2013–2020, suggesting a potential correlation among these environmental factors. The uneven spatial distribution of UV radiation was associated with factors such as latitude, elevation, meteorological factors, and season. The eastern areas of China pose a higher risk due to both high population density and high UV radiation intensity. Using a machine learning algorithm, this study generated a gridded UV radiation dataset with extensive spatiotemporal coverage, which can be utilized for future health-related research. This dataset is freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024).

Sweeping jet actuator (SJA) has been widely applied for active flow control in open flows. In this paper, the SJA character in compressor cascade and its performance for separation control in inner flows are discussed. Time-averaged and transient flow field measurement, together with visualization methods are utilized. It is found that endwall effects are important for both SJA behaviors and SJA performance for separation control in compressor cascades. There is a maximum of 12.7% total pressure loss reduction with SJA placed near the separation position, close to the endwall and under appropriate flowrate. The characteristic frequencies in the flow field contribute to the capture of influence regions of vortices and excitation jets. Two concentrated shedding vortices and SJA jets impact region helped to judge that SJA energizes low momentum fluids in a large region and matches the high loss core well. To be concrete, the flow separation control mechanism of SJA lies on the interruption of the blade suction surface boundary layer development and the restriction of the lifting of the boundary layer from endwall towards blade suction surface.

China has undergone rapid urbanization and internal migration in the past few years, and its up-to-date gridded population datasets are essential for various applications. Existing datasets for China, however, suffer from either outdatedness or failure to incorporate data from the latest Seventh National Population Census of China, conducted in 2020. In this study, we develop a novel population downscaling approach that leverages stacking ensemble learning and big geospatial data to produce up-to-date population grids at a 100 m resolution for China using seventh census data at both county and town levels. The proposed approach employs stacking ensemble learning to integrate the strengths of random forest, XGBoost, and LightGBM through fusing their predictions in a training mechanism, and it delineates the inhabited areas from big geospatial data to enhance the gridded population estimation. Experimental results demonstrate that the proposed approach exhibits the best-fit performance compared to individual base models. Meanwhile, the out-of-sample town-level test set indicates that the estimated gridded population dataset (R2=0.8936) is more accurate than existing WorldPop (R2=0.7427) and LandScan (R2=0.7165) products for China in 2020. Furthermore, with the inhabited area enhancement, the spatial distribution of population grids is intuitively more reasonable than the two existing products. Hence, the proposed population downscaling approach provides a valuable option for producing gridded population datasets. The estimated 100 m gridded population dataset of China holds great significance for future applications, and it is publicly available at https://doi.org/10.6084/m9.figshare.24916140.v1 (Chen et al., 2024b).

Active faults serve as potential sources of destructive earthquakes. Studies and investigations of active faults are necessary for earthquake disaster prevention. This study presents a nation-scale database of active faults in China and its adjacent regions, in tandem with an associated web-based query system. This database is an updated version of the active faults data included in the Seismotectonic Map of China and its Adjacent Regions (1:4 000 000), which is one of the four essential maps of the mandatory Chinese standard GB 18306-2015 Seismic Ground Motion Parameter Zonation Maps of China. The data update and integration stem from regional-scale studies and surveys conducted over the past 2 decades (at reference scales from 1:250 000 to 1:50 000). The information amassed from these regional-scale studies and surveys encompasses geophysical probing, drill logging, measurement of offset landforms, sample dating, as well as geometric and kinematic parameters of exposed and blind faults, paleo-earthquake sequences, and recurrence intervals. These data have been acquired and analyzed utilizing a uniform technical standard framework and reviewed by expert panels in both field and laboratory settings. Our system hosts this nation-scale database accessible through a Web Geographic Information System (GIS) application, enabling browsing, querying, and downloading functionalities via a web browser. The system we built also publishes the Open Geospatial Consortium (OGC) Web Feature Service and the OGC Web Map Service of active faults data. Users can incorporate map layers and obtain fault data in OGC-compliant GIS software for further analysis through these services. The Chinese government, research institutions, and companies have widely used the active faults data from the previous versions of the database. The database is available at https://doi.org/10.12031/activefault.china.400.2023.db (Xu, 2023) and via the web system (https://data.activetectonics.cn/arcportal/apps/webappviewer/index.html?id=684737e8849c4170bbca14447608c451, CEFIS, 2023; http://data.activetectonics.cn/arcserver/services/Hosted/CAFD400_2022_WFS/MapServer/WFSServer, CAFD WFS, 2024).

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 over eastern China, with GFDL-ESM4 (−1.5 %) and MPI-ESM-1-2-HAM (−1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM2.5 from the available component output, underestimate the total PM2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by −71.2 % to −37.8 % across the 14 models) and the smallest for BC (−47.9 % to −12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.74) fairly well, yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing.

Fire is a fundamental part of the Earth system, with impacts on vegetation structure, biomass, and community composition, the latter mediated in part via key fire-tolerance traits, such as bark thickness. Due to anthropogenic climate change and land use pressure, fire regimes are changing across the world, and fire risk has already increased across much of the tropics. Projecting the impacts of these changes at global scales requires that we capture the selective force of fire on vegetation distribution through vegetation functional traits and size structure. We have adapted the fire behavior and effects module, SPITFIRE (SPread and InTensity of FIRE), for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime, and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf, and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown, and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown, and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees, and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire-burned area and its effects, which could feed back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m−1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m−1. The results support a positive grass–fire feedback across the region and suggest that forests which have existed without frequent burning may be vulnerable at higher fire intensities, which is of greater concern under intensifying climate and land use pressures. The ability of FATES to capture the connection between fire disturbance and plant fire-tolerance strategies in determining biogeography provides a useful tool for assessing the vulnerability and resilience of these critical carbon storage areas under changing conditions across the tropics.

The illegal occupation of farmland for building houses has impact not only on the land resources, but also the agricultural production, ecological environment, and food security. In order to protect farmland resources, maintain stable agricultural production activities and improve ecological environment, this paper designs and implements a time-point approval and dynamic monitoring solution and system for the problem of rural illegal occupation of farmland for constructing houses by comprehensively utilizing the satellite remote sensing technology (RS) and geographic information system technology (GIS). The result shows that the time-point approval and dynamic monitoring system can identify the illegal construction of houses accurately, thus this work can curb the generation of new illegal houses, and support the special governance of the construction of illegally occupied farmland effectively and continuously.

Spaceborne laser altimetry represents a novel active remote sensing technology applicable to earth observation, which together with imaging spectroscopy and synthetic aperture radar as a core technology for data acquisition in the earth observation systems. However, the accuracy of horizontal positioning for laser footprints from spaceborne laser altimeters declines due to various factors such as the changes in the orbital environment and the deterioration of performance. Moreover, the limited frequency of in-orbit calibration of the spaceborne laser altimeters and the non-disclosure of calibration parameters mean that users are heavily reliant on positioning accuracy of the altimetry data provided. To address this issue, a new algorithm is proposed in this study for enhancing the accuracy of horizontal positioning for laser footprints in the absence of satellite altimeter pointing and ranging parameters. In this algorithm, high-resolution DSM is taken as the reference terrain data to take advantage of the higher precision in elevation over horizontal positioning of the laser footprints. By adjusting the horizontal position of the laser footprint within a small area, the algorithm achieves the optimal alignment of laser elevation data with the reference terrain. Then, the resulting shift in the horizontal position of the laser footprints is referenced to correct their horizontal positioning during that period. Based on the high-accuracy DSM data collected from the Xinjiang autonomous region in China and the data collected by the GF-7 satellite, simulation experiments are performed in this study to analyze and validate the proposed algorithm. According to the experimental results, the horizontal accuracy of the laser footprints improves significantly from 12.56 m to 3.11 m after optimization by the proposed method. With the elimination of 9.45 m horizontal error, accuracy is improved by 75.23%. This method is demonstrated as effective in further optimizing the horizontal position of laser altimetry data products in the absence of altimeter parameters and original data, which promotes the application of spaceborne laser data.

Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in ∼ 100 000 existing reservoirs. Here, we use the finest-spatiotemporal-resolution (1 d and 1 km) precipitation dataset from an ensemble of machine learning algorithms to present the spatial distribution of 1 d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find that the national PMP generally decreases from southeast to northwest and is typically dominated by the high variability of precipitation extremes in northern China and high intensity in southern China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing them with satellite and in situ results due to differences in spatial scales and computation methods. Interannual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land–atmosphere coupling effects, leads to a widespread increase (> 20 %) in PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emissions. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

To investigate the perforating characteristics of Zr-based bulk metallic glass fragments against thin steel target, ballistic experiments were carried out to measure ballistic the limit velocities of fragments perforating against different thicknesses of targets. The fragments were driven by a 57 mm one-stage light gas gun to impact the thin steel target. The numerical model of Zr-based bulk metallic glass fragments perforating against thin steel target was established using Autodyn2D commercial software, and the perforating process and perforating mechanism were analyzed by numerical simulation. The semi-empirical model to predict the ballistic limit velocity was fitted. An energy model for calculating the residual velocity of a fragment was derived based on the conservation of energy. The theoretical results of the semi-empirical model and the energy model are in good agreement with experiment results and simulation results. And the energy model has good applicability without fitting the parameters for different target thicknesses.

The bullet shape is critical in efficient bullet design because it affects the lift and drag forces. This paper proposes a new bullet shape with a logarithmic curve and analyzes the lift and drag coefficients of bullets with different curves under different angles of attack. The results are compared with a bullet whose shape is described by the power law curve. Fluent simulations demonstrate that the optimal power exponent values are 0.65, 0.6, and 0.65 for the bullet with the power law curve and 1.3, 1, and 1 for the bullet with the logarithmic curve at 0°, 30°, and 40° angles of attack, respectively. At a 0° angle of attack, the lift coefficient of the logarithmic curve is the largest. The lift force of the bullet with the logarithmic curve is 129.4% higher than that with the von Karman curve. The drag coefficient is the largest for the bullet with the rectilinear curve; it is 1.30% larger than that of the bullet with the logarithmic curve. At 30° and 40° angles of attack, the lift coefficient of the bullet with the power law curve is larger. The difference in the lift coefficients between the two angles of attack is 18.47%. The bullet’s drag coefficient is the largest for the logarithmic curve, and the difference in the drag coefficients between the two angles of attack is 18.59%.

In this paper, SiCp/TC11 titanium matrix composites were prepared by selective laser melting. The influence of laser process parameters on the forming quality of composites was studied by control variable method. The results show that the process parameters have a significant effect on the forming quality of the composite material. The laser power has the greatest influence on the density, followed by the scanning spacing, and the scanning speed has a relatively small influence. When the laser power is 160 ~ 180 W, the scanning speed is 1 000 ~ 1 200 mm/s, and the scanning spacing is 0,1 mm, the forming quality of the sample is better.

This study focused on the microstructure evolution of non-oriented Fe-3,0 %Si steel used in the core of new energy vehicle drive motor. An hot band and a sample normalized at 800 °C for 10 min were subjected to a moderate cold rolling and an annealing treatment. The results show that the initial hot band has gradient microstructure. After normalizing, the surface is consisted of fine recrystallized grains, the central layer has elongated α-fiber and γ-fiber grains, the subsurface shows a mixed grain structure with strong Goss texture. After cold rolling with a thickness reduction of 40 %, α-fiber and γ-fiber textures are strengthened, and unstable Goss texture disappears. After annealing at 700 °C for 5 min, gradient recrystallization occurs and γ-fiber texture is weakened. The central and subsurface layers show frequently nucleation phenomena in the grain interior and grain boundaries, resulting in strong Ѳ-fiber, α*-fiber and Goss components.

Forest biodiversity is essential in maintaining ecosystem functions and services. Recently, unmanned aerial vehicle (UAV) remote sensing technology has emerged as a cost-effective and flexible tool for biodiversity monitoring. In this study, we compared the optimal clustering algorithm, classification method (spectral angle mapper, SAM), spectral diversity metric and structural heterogeneity index for forest species diversity estimation in two complex subtropical forests, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves in China, using UAV-borne hyperspectral and LiDAR data. The results showed that the SAM classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (MZL: 0.62 > 0.46 and GGS: 0.55 > 0.46) and Shannon-Wiener index (MZL: 0.64 > 0.58 and GGS: 0.52 > 0.47), while the optimal clustering algorithm had the highest prediction accuracy for the Simpson index, followed by the SAM classification method, spectral diversity metric and structural heterogeneity index (MZL: 0.83>0.44>0.31>0.12, GGS: 0.62>0.44>0.38>0.00). Our study indicated that the SAM classification method had the advantage of identifying rare species and estimating species richness, while the clustering method could capture forest diversity patterns rapidly without distinguishing the specific tree species and predict the Simpson index more accurately. Overall, both clustering and classification methods exhibited superior performance compared to spectral or structural diversity indices. Our findings highlight the applicability of UAV remote sensing in monitoring forest species diversity in complex subtropical forests.

Abstract Boron (B) isotopes are widely used in the Earth sciences to trace processes ranging from slab recycling in the mantle to changes in ocean pH and atmospheric CO2. Boron isotope analysis is increasingly achieved by multi‐collector inductively coupled plasma mass spectrometry, which requires separation of B from the sample matrix. Traditional column chromatography methods for this separation have a well‐established track record but are time consuming and prone to contamination from airborne blank. Here, we present an extensive array of tests that establish a novel method for B purification using a batch method. We discuss the key controls and limitations on sample loading, matrix removal and B elution including sample volume, ionic strength, buffer to acid ratio and elution volume, all of which may also help optimize column‐based methods. We find consistent, low procedural blanks of 10 ± 16 pg and excellent reproducibility: 10 ng NIST RM 8301 foram [8301f] yields 14.58 ± 0.11‰ 2SD n = 15; 2.5 ng 8301f yields 14.60 ± 0.19‰ 2SD, n = 31; and overall long term 2SD on n = 218 samples pooling different sample sizes yields 14.62 ± 0.21‰ 2SD. This method also offers significant advantages in throughput, allowing the processing of 24 samples in ∼5 hr. This boron batch method thus provides a fast, reproducible, low‐blank method for purification of boron for high precision isotopic analyses.

Extreme droughts are a major determinant of ecosystem disturbance that impacts plant communities and feeds back into climate change through changes in plant functioning. However, the complex relationships between aboveground and belowground plant hydraulic traits and their role in governing plant responses to drought are not fully understood. In this study, we use a model, the Functionally Assembled Terrestrial Ecosystem Simulator in a configuration that includes plant hydraulics (FATES-Hydro), to investigate ecosystem responses to the 2012–2015 California drought in comparison with observations at a site in the southern Sierra Nevada that experienced widespread tree mortality during this drought. We conduct a sensitivity analysis to explore how different plant water sourcing and hydraulic strategies lead to differential responses during normal and drought conditions. The analysis shows the following. Deep roots that sustain productivity through the dry season are needed for the model to capture observed seasonal cycles of evapotranspiration (ET) and gross primary productivity (GPP) in normal years, and deep-rooted strategies are nonetheless subject to large reductions in ET and GPP when the deep soil reservoir is depleted during extreme droughts, in agreement with observations. Risky stomatal strategies lead to greater productivity during normal years as compared to safer stomatal control, but they also lead to a high risk of xylem embolism during the 2012–2015 drought. For a given stand density, stomatal and xylem traits have a stronger impact on plant water status than on ecosystem-level fluxes. Our study highlights the significance of resolving plant water sourcing strategies to represent drought impacts on plants and consequent feedbacks in models.

Vegetation plays a key role in the global carbon cycle and thus is an important component within Earth system models (ESMs) that project future climate. Many ESMs are adopting methods to resolve plant size and ecosystem disturbance history, using vegetation demographic models. These models make it feasible to conduct more realistic simulation of processes that control vegetation dynamics. Meanwhile, increasing understanding of the processes governing plant water use, and ecosystem responses to drought in particular, has led to the adoption of dynamic plant water transport (i.e., hydrodynamic) schemes within ESMs. However, the extent to which variations in plant hydraulic traits affect both plant water stress and the risk of mortality in trait-diverse tropical forests is understudied. In this study, we report on a sensitivity analysis of an existing hydrodynamic scheme (HYDRO) model that is updated and incorporated into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (FATES–HYDRO V1.0). The size- and canopy-structured representation within FATES is able to simulate how plant size and hydraulic traits affect vegetation dynamics and carbon–water fluxes. To better understand this new model system, and its functionality in tropical forest systems in particular, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We assembled 942 observations of plant hydraulic traits on 306 tropical plant species for stomata, leaves, stems, and roots and determined the best-fit statistical distribution for each trait, which was used in model parameter sampling to assess the parametric sensitivity. We showed that, for simulated leaf water potential and loss of hydraulic conductivity across different plant organs, the four most important traits were associated with xylem conduit taper (buffers increasing hydraulic resistance with tree height), stomatal sensitivity to leaf water potential, maximum stem hydraulic conductivity, and the partitioning of total hydraulic resistance above vs. belowground. Our analysis of individual ensemble members revealed that trees at a high risk of hydraulic failure and potential tree mortality generally have a lower conduit taper, lower maximum xylem conductivity, lower stomatal sensitivity to leaf water potential, and lower resistance to xylem embolism for stem and transporting roots. We expect that our results will provide guidance on future modeling studies using plant hydrodynamic models to predict the forest responses to droughts and future field campaigns that aim to better parameterize plant hydrodynamic models.

The origin of unique parallel alignment of (0001) poles to transverse direction (TD) was investigated using Mg-3Al-0.4Mn (mass%) alloy sheets rolled with different process conditions. When rolling was performed with intermediate reheating, the alloy showed a sluggish static recrystallization (SRX) behavior during post-annealing, facilitating the nucleation and growth of statically recrystallized grains from extension twins. This resulted in the apparent texture component with the parallel alignment of the (0001) poles to the TD, and the sheet exhibited good ductility for both the rolling direction (RD) and TD. In contrast, continuous rolling without intermediate reheating led to the formation of severely deformed regions near double twins. SRX was promoted at such regions, forming a typical basal textural feature with weak RD-split of the (0001) poles. Although extension twins were formed after the continuous rolling, SRX was facilitated at the severely deformed regions with double twins, and the formation of the unique alignment of the (0001) poles to the TD was suppressed. The RD-split texture led to the large elongation to failure along the RD, while it along the TD decreased owing to the narrow distribution of the (0001) poles toward the TD, resulting in the in-plane anisotropy in ductility.

Soil pore water (SPW) chemistry can vary substantially across multiple scales in Arctic permafrost landscapes. The magnitude of these variations and their relationship to scale are critical considerations for understanding current controls on geochemical cycling and for predicting future changes. These aspects are especially important for Arctic change modeling where accurate representation of sub-grid variability may be necessary to predict watershed-scale behaviors. Our research goal is to characterize intra- and inter-watershed soil water geochemical variations at two contrasting locations in the Seward Peninsula of Alaska, USA. We then attempt to identify the key factors controlling concentrations of important pore water solutes in these systems. The SPW geochemistry of 18 locations spanning two small Arctic catchments was examined for spatial variability and its dominant environmental controls. The primary environmental controls considered were vegetation, soil moisture and/or redox condition, water–soil interactions and hydrologic transport, and mineral solubility. The sampling locations varied in terms of vegetation type and canopy height, presence or absence of near-surface permafrost, soil moisture, and hillslope position. Vegetation was found to have a significant impact on SPW NO3- concentrations, associated with the localized presence of nitrogen-fixing alders and mineralization and nitrification of leaf litter from tall willow shrubs. The elevated NO3- concentrations were, however, frequently equipoised by increased microbial denitrification in regions with sufficient moisture to support it. Vegetation also had an observable impact on soil-moisture-sensitive constituents, but the effect was less significant. The redox conditions in both catchments were generally limited by Fe reduction, seemingly well-buffered by a cache of amorphous Fe hydroxides, with the most reducing conditions found at sampling locations with the highest soil moisture content. Non-redox-sensitive cations were affected by a wide variety of water–soil interactions that affect mineral solubility and transport. Identification of the dominant controls on current SPW hydrogeochemistry allows for qualitative prediction of future geochemical trends in small Arctic catchments that are likely to experience warming and permafrost thaw. As source areas for geochemical fluxes to the broader Arctic hydrologic system, geochemical processes occurring in these environments are particularly important to understand and predict with regards to such environmental changes.

To enhance the timeliness and accuracy of spatial prediction of coseismic landslides, we propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software named Mat.LShazard V1.0. Based on this software, we evaluate the applicability of this improved spatial prediction strategy in six earthquake events that have occurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian, Lushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that in the first stage (immediately after the quake event), except for the 2013 Minxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan earthquake is the highest, reaching 0.947. The prediction results in the first stage can meet the requirements of emergency rescue by immediately obtaining the overall predicted information of the possible coseismic landslide locations in the quake-affected area. In the second and third stages, with the improvement of landslide data quality, the prediction ability of the model based on the entire landslide database is gradually improved. Based on the entire landslide database, the AUC value of the six events exceeds 0.9, indicating a very high prediction accuracy. For the second and third stages, the predicted landslide area (Ap) is relatively consistent with the observed landslide area (Ao). However, based on the incomplete landslide data in the meizoseismal area, Ap is much smaller than Ao. When the prediction model based on complete landslide data is built, Ap is nearly identical to Ao. This study provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages of emergency rescue, temporary resettlement, and late reconstruction after a major earthquake.

Runoff formation and hydrologic regulation mechanisms in mountainous cold regions are the basis for investigating the response patterns of hydrological processes under climate change. Because of plate movements and climatic effects, the surface soils of bare lands and grasslands on the Qinghai–Tibet Plateau (QTP) are thin, and the soil below the surface contains abundant gravel. This characteristic geological structure, combined with snow and frozen soil, affects the water cycle in this region. To investigate the influence of the underlying surface structure on water–heat transport and water circulation processes on the QTP, a comprehensive study was performed combining water–heat transfer field experiments, and a water and energy transfer process model for the QTP (WEP-QTP) was developed based on the original water and energy transfer process model in cold regions (WEP-COR). The Niyang River basin, located on the QTP, was selected as the study area to evaluate the consistency between theoretical hypotheses, observations, and modeling results. The model divided the uniform soil profile into a dualistic soil–gravel structure. When no phase change was present in the ground, two infiltration models based on the dualistic soil–gravel structure were developed; these used the Richards equation to model a non-heavy rain scenario and the multilayer Green–Ampt model for a heavy rain scenario. During the freeze–thaw period, a water–heat coupling model based on the snow–soil–gravel layer structure was constructed. By considering gravel, the improved model corrected the overestimation of the moisture content below the surface soil predicted by the original model and reduced the moisture content relative error (RE) from 33.74 % to −12.11 %. The addition of the snow layer not only reduced the temperature fluctuation of the surface soil, but also revised the overestimation of the freeze–thaw speed predicted by the original model with the help of the gravel. The temperature root-mean-square error was reduced from 1.16 to 0.86 ∘C. In the fully thawed period, the dualistic soil–gravel structure improved the regulation effect of groundwater on flow, thus stabilizing the flow process. The maximum RE at the flow peak and trough decreased by 88.2 % and 21.3 %, respectively. In the freeze–thaw period, by considering the effect of the snow–soil–gravel layer structure, the freezing and thawing processes of WEP-QTP lagged behind those of WEP-COR by approximately 1 month. The groundwater simulated by WEP-QTP had more time to recharge the river, which better represented the observed “tailing” process from September onwards. The flow simulated by the WEP-QTP model was more accurate and closer to the actual measurements, with Nash–Sutcliffe efficiency > 0.75 and |RE| < 10 %. The improved model reflects the effects of the typical QTP environment on water–heat transport and water cycling and can thus be used for hydrological simulation on the QTP.

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed an end-to-end programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. As a genre of physics-informed machine learning (ML), differentiable models couple physics-based formulations to neural networks (NNs) that learn parameterizations (and potentially processes) from observations, here photosynthesis rates. We first demonstrated that the framework was able to correctly recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types (PFTs), we learned parameters that performed substantially better and greatly reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 ∘C (Vc,max25) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

Three-dimensional excitation–emission matrix (EEM) fluorescence spectroscopy is an important method for the identification of the occurrence, chemical composition, and source of atmospheric chromophores. However, current knowledge on the identification and interpretation of fluorescent components is mainly based on aquatic dissolved organic matter and might not be applicable to atmospheric samples. Therefore, this study comprehensively investigated EEM data of different types of strong light-absorbing organic compounds, water-soluble organic matter (WSOM) in different aerosol samples (combustion source samples and ambient aerosols), soil dust, and purified fulvic and humic acids supplemented by parallel factor (PARAFAC) modeling. The results demonstrated that organic compounds with high aromaticity and strong electron-donating groups generally present strong fluorescence spectra at longer emission wavelengths, whereas organic compounds substituted with electron-withdrawing groups have relatively weaker fluorescence intensity. In particular, aromatic compounds containing nitro groups (i.e., nitrophenols), which show strong absorption and are the major component of atmospheric brown carbon, exhibited no significant fluorescence. The EEM–PARAFAC method identified three fluorescent components (i.e., C1, C2, and C3) in ambient WSOM. Although EEM–PARAFAC-derived C1 (Ex/Em = 235, 270/330 nm) in ambient WSOM is generally considered to be protein-like groups, our findings suggested that it is mainly composed of aromatic acids, phenolic compounds, and their derivatives, with only traces of amino acids. C2 is associated with the atmospheric chemical reaction of biomass burning and/or biogenic organic molecules, with a relatively lower degree of oxidation, which are more abundant in Guangzhou WSOM (56 %–69 %). C3, in contrast, is mainly attributed to highly oxygenated organic molecules derived from soil and atmospheric aging processes and has a relatively higher contribution in Chuzhou WSOM (23 %). These findings provide new insights into the analysis of chemical properties and sources of atmospheric fluorophores using the EEM method.

The summertime low-oxygen conditions in the Pearl River Estuary (PRE) have experienced a significant spatial expansion associated with notable deoxygenation in recent decades. Nevertheless, there is still a lack of quantitative data on the long-term trends and interannual variabilities in oxygen conditions in the PRE as well as on the driving factors. Therefore, the long-term deoxygenation in a subregion of the PRE (the coastal waters off Hong Kong) was comprehensively investigated in this study using monthly observations during 1994–2018. To evaluate the changes in scope and intensity of oxygen conditions, an indicator (defined as the low-oxygen index, LOI) that integrates several metrics related to low-oxygen conditions was introduced as the result of a principal component analysis (PCA). Moreover, primary physical and biogeochemical factors controlling the interannual variabilities and long-term trends in oxygen conditions were discerned, and their relative contributions were quantified by multiple regression analysis. Results showed that the regression models explained over 60 % of the interannual variations in LOI. Both the wind speeds and concentrations of dissolved inorganic nitrogen (DIN) played a significant role in determining the interannual variations (by 39 % and 49 %, respectively) and long-term trends (by 39 % and 56 %, respectively) in LOI. Due to the increasing nutrient loads and alterations in physical conditions (e.g., the long-term decreasing trend in wind speeds), coastal eutrophication was exaggerated and massive marine-sourced organic matter was subsequently produced, thereby resulting in an expansion of intensified low-oxygen conditions. The deteriorating eutrophication has also driven a shift in the dominant source of organic matter from terrestrial inputs to in situ primary production, which has probably led to an earlier onset of hypoxia in summer. In summary, the Hong Kong waters have undergone considerable deterioration of low-oxygen conditions driven by substantial changes in anthropogenic eutrophication and external physical factors.

Microstructure evolution and mechanical properties of the as-drawn dilute Mg-0.8Al-0.1Ca-0.6Mn (wt. %) alloy wires during annealing and peak-aging treatment were investigated in this paper. The results show that the high density of nano-sized Al–Mn precipitates strongly hindered grain boundary migration to restrict grain growth during annealing under 300 °C and 400 °C, while grain growth was obvious and the uniformity of recrystallization grain size increased due to partial dissolution of Al–Mn precipitates at 500 °C. In addition, there was a growth preference in grains with (0001) orientation, which was closely associated with the texture changes. The alloy annealed at 500 °C for 10min exhibits remarkable age-hardening response and both the tensile yield strength and elongation to failure were improved simultaneously after peak-aging treatment, which was mainly attributed to the monolayered Guinier-Preston (G.P.) zones and planar Al2Ca precipitates.

With the gradual warming of the global climate, frequent floods have caused huge losses to human life and property. Flood mapping by SAR image has been an important topic, and it is increasingly important to use deep learning method to extract flood information. In order to achieve automatic flood extent extraction, this paper proposes an attention mechanism-based water body extraction network with GF3 images, and successfully use it for flood detection in Xinxiang, Henan, China. In this paper, the proposed network incorporates the channel attention mechanism and position attention mechanism based on U-Net, to improve the efficiency and accuracy of water extraction, ignore the unimportant information by learning the weight and make the model focus toward the important information related to the water body. The OA of our method can reach 0.959 and Recall can reach 0.942 by verifying four sets of test data. Experiments show fast flood mapping in Xinxiang can be achieved by our method.

Strength and ductility synergy in an Mg-3mass%Al-Mn (AM30) alloy sheet was successfully improved via twin-roll casting and annealing at low-temperature. An AM30 alloy sheet produced by twin-roll casting, homogenization, hot-rolling, and subsequent annealing at 170 °C for 64 h exhibits a good 0.2% proof stress of 170 MPa and a large elongation to failure of 33.1% along the rolling direction. The sheet also shows in-plane isotropic tensile properties, and the 0.2% proof stress and elongation to failure along the transverse direction are 176 MPa and 35.5%, respectively. Though the sheet produced by direct-chill casting also shows moderate strengths if the annealing condition is same, the direct-chill casting leads to the deteriorated elongation to failure of 23.9% and 30.0% for the rolling and transverse directions, respectively. As well as such excellent tensile properties, a high room-temperature stretch formability with an Index Erichsen value of 8.3 mm could be obtained in the twin-roll cast sheet annealed at 170 °C for 64 h. The annealing at a higher temperature further improves the stretch formability; however, this results in the decrease of the tensile properties. Microstructure characterization reveals that the excellent combination of strengths, ductility, and stretch formability in the twin-roll cast sheet annealed at the low-temperature annealing is mainly attributed to the uniform recrystallized microstructure, fine grain size, and circular distribution of (0001) poles away from the normal direction of the sheet.

Decabromodiphenyl ether (BDE-209), a major component of brominated flame retardants, has been detected in considerable amounts in the soil. Given that BDE-209 is toxic, ubiquitous, and persistent, it may cause toxic effects on vegetables planted in contaminated soil. In this study, Chinese cabbage seedlings cultivated in the soil contaminated by BDE-209 (5 - 20 mg kg-1) for 60 d were investigated to evaluate the phytotoxicity of BDE-209 in terms of growth, physiological responses, photosynthetic function, and antioxidant capacity. The results showed that BDE-209-induced phytotoxicity was reflected in the growth suppression, the decrease of chlorophyll content and soluble protein content, and especially in the reduced photosynthetic parameters (net photosynthetic rate and stomatal conductance). BDE-209 showed direct toxicities to plasma membranes causing their elevated permeability. In addition, BDE-209 induced the overproduction of reactive oxygen species (ROS), membrane lipid peroxidation and protein carbonylation, manifesting in the increased content of O2*-, H2O2, malondialdehyde, and carbonyl. Chinese cabbage seedlings activated the antioxidant defence system (superoxide dismutase and peroxidase) to scavenge the ROS and counter-balance the oxidative stress caused by BDE-209, while the toxicity could not be effectively alleviated. Our study will provide valuable information for further understanding of the phytotoxicity of polybrominated diphenyl ethers.

ABSTRACT: This experiment investigated the effects of different dietary starch sources on the growth and glucose metabolism of geese. A total of 240 healthy 35-day-old male geese were selected and randomly divided into 4 groups, with 6 replicates per group and 10 geese per replicate. Four types of diets were prepared, with glutinous rice (rapidly-digestible starch), corn, indica rice and high amylose as their starch sources, and fed for 28 d. Results showed that after consuming different feeds, the blood glucose of geese first increased and then decreased, reaching its maximum value 0.5 h after feeding, and there were significant differences between the groups (P < 0.05). The body weight of the corn and indica rice group geese at 63 d was higher than that of the high amylose group (P < 0.05). The serum total cholesterol (TCHO) content in the glutinous rice and corn groups was higher than in the high amylose group (P < 0.05). The serum insulin content in the glutinous rice group was lower than in the corn and high amylose groups (P < 0.05), while the glucagon content was higher (P < 0.05). The α-amylase activities of the pancreas, jejunal chyme, and jejunal mucosa in the glutinous rice group were higher than in the indica rice and high amylose groups (P < 0.05). The liver glycogen content in the glutinous rice group was higher than the other groups (P < 0.05). The liver glucose-6-phosphate dehydrogenase (G-6-PD) content in the glutinous rice group was higher than the high amylose group's (P < 0.05), but the glycogen synthase kinase-3 β (GSK-3β) content was lower (P < 0.05). In conclusion, the corn and indica rice diets had a positive effect on the growth performance of the geese, while the high amylose diet had a negative effect. The glutinous rice diet leads to rapid release of glucose, strengthening glucose metabolism pathways such as glycogen synthesis and the pentose phosphate pathway, and further influencing lipid metabolism.

A wide range of materials including mineral dust, soil dust, and bioaerosols have been shown to act as ice nuclei in the atmosphere. During atmospheric transport, these materials can become coated with inorganic and organic solutes which may impact their ability to nucleate ice. While a number of studies have investigated the impact of solutes at low concentrations on ice nucleation by mineral dusts, very few studies have examined their impact on non-mineral dust ice nuclei. We studied the effect of dilute (NH4)2SO4 solutions (0.05 M) on immersion freezing of a variety of non-mineral dust ice-nucleating substances (INSs) including bacteria, fungi, sea ice diatom exudates, sea surface microlayer substances, and humic substances using the droplet-freezing technique. We also studied the effect of (NH4)2SO4 solutions (0.05 M) on the immersion freezing of several types of mineral dust particles for comparison purposes. (NH4)2SO4 had no effect on the median freezing temperature (ΔT50) of 9 of the 10 non-mineral dust materials tested. There was a small but statistically significant decrease in ΔT50 (−0.43 ± 0.19 ∘C) for the bacteria Xanthomonas campestris in the presence of (NH4)2SO4 compared to pure water. Conversely, (NH4)2SO4 increased the median freezing temperature of four different mineral dusts (potassium-rich feldspar, Arizona Test Dust, kaolinite, montmorillonite) by 3 to 9 ∘C and increased the ice nucleation active site density per gram of material (nm(T)) by a factor of ∼ 10 to ∼ 30. This significant difference in the response of mineral dust and non-mineral dust ice-nucleating substances when exposed to (NH4)2SO4 suggests that they nucleate ice and/or interact with (NH4)2SO4 via different mechanisms. This difference suggests that the relative importance of mineral dust to non-mineral dust particles for ice nucleation in mixed-phase clouds could potentially increase as these particles become coated with (NH4)2SO4 in the atmosphere. This difference also suggests that the addition of (NH4)2SO4 (0.05 M) to atmospheric samples of unknown composition could potentially be used as an indicator or assay for the presence of mineral dust ice nuclei, although additional studies are still needed as a function of INS concentration to confirm the same trends are observed for different INS concentrations than those used here. A comparison with results in the literature does suggest that our results may be applicable to a range of mineral dust and non-mineral dust INS concentrations.

The static recrystallization and associated texture evolution were investigated in an extruded Mg-Zn-Gd alloy with bimodal microstructure based on a quasi-in-situ electron back-scatter diffraction (EBSD) method. The typical rare earth (RE) texture formed during annealing, evolving from the bimodal microstructure with [101¯0] basal fiber texture that consisted of fine recrystallized (RXed) grains and coarse unrecrystallized (unRXed) grains elongated along the extrusion direction. In both RXed and unRXed regions, the RXed nucleation produced randomized orientations without preferred selection and the RXed grains with RE texture orientation had more intensive growth ability than those with basal fiber orientation, thereby leading to the preferred selection of RE texture orientation during grain growth. The relationships between stored strain energy, solute drag, grain growth and texture evolution are discussed in detail. This study provided direct evidence of the RE texture evolution in an extruded Mg-RE alloy, which assists in understanding the formation mechanisms for RE texture during extrusion and better developing wrought Mg alloys with improved formability.

In the past two decades, landscape archaeology has undergone a paradigm shift from traditional theoretical methods to being practically oriented, with the advent of the widespread application of philosophical theories (such as phenomenology, hermeneutics, and others) and the emerging new technologies in social sciences. Nevertheless, landscape archaeology has not been able to garner the attention it requires from Chinese archaeology, which fails to understand its significance behind the systematic regional survey methods. Rather, for a long time, the study of the man-land relationship has been considered to be a part of environmental archaeology. Besides, the landscape elements in archaeological excavations were often considered as mechanical interactions between people and the environment, resulting in a lack of holistic and systematic research on a selection of archaeological sites. The focus however has remained restricted to the earthen remains and relics in the archaeological process. The Northern Wei Dynasty was the first nomadic regime to control the Central Plains in the Chinese history and moved its capital three times for the purpose of sinicization. The recent archaeological excavations of the ancient city of Shengle, imperial palaces, tombs, sacrificial sites, gardens, Yinshan palaces, and the border defense facilities during the Shengle period of the Northern Wei Dynasty have revealed several phenomena and evidence of the cultural integration of the various ethnic groups. As mentioned earlier, the limitations in the research horizon have led to the in-depth analysis and research of archaeological relics and archaeological data during this period seeking the desired attention. This study considers landscape archaeology, anthropology, sociology, and history as the primary research methods pertinent to the above situation. It considers archaeological relics and archaeological data from the prosperous period as the research object and thoroughly analyses the relationship between the people and the earthen landscape relics, to reveal the social culture, the religious beliefs, the politics, and the military behind the integration of the multi-ethnic culture, along with the cognition of the natural environments, the social structures, and the religious spaces. Simultaneously, the analysis results would also endeavor to integrate the artifacts, the relics (space, structure, layout, and locational relationship), road grids, surrounding environment, and several other surface space elements to restore and reproduce the prosperous social and cultural situations scenes of the bygone period. The final outcome shall become a typical research case. By comparing and combing the archaeological discoveries of the Northern and the Southern Dynasties of China and the pertinent archaeological data, we could further understand and explain the multi-ethnic cultural development and evolution while providing an essential theoretical basis for the present social and cultural research on the Northern Wei Dynasty in China.

ABSTRACT The objective of the present study was to investigate the different plasma metabolites between anestrus and estrus postpartum dairy cows and to provide a theoretical basis for prevention of anestrus in dairy farm cows. In the experiment, one hundred and sixty-seven Holstein dairy cows were selected with similar age and parity. According to the concentration of β-hydroxybutyric acid, non-esterified fatty acids and glucose in plasma during 14 to 21 days in milk, all dairy cows were determined as having a status of energy balance. According to the results of clinical symptom, rectal and B ultrasound examination at 60 to 90 days postpartum, these cows were divided into twenty estrus and twenty-four anestrus group, other dairy cows were removed. 1H nuclear magnetic resonance technology was utilized to detect the plasma metabolites changes and screen different plasma metabolites between anestrus and estrus cows. Ten different metabolites including alanine, glutamic acid, asparagine, creatine, choline, phosphocholine, glycerophosphocholine, low-density lipoprotein, and very-low-density lipoprotein were significantly decreased in anestrous cows compared with estrous cows. Metabolic pathway analyses indicated that differential metabolites were primarily involved in amino acid and glycerophospholipid metabolism. These metabolites and their enrichment pathways indicate that reduced steroid hormone synthesis precursors result in lower levels of estradiol and progesterone and cause anestrus in negative energy balance. These data provide a better understanding of the changes that may affect estrus of postpartum dairy cows at NEB status and lay the ground for further research.

Live fuel moisture content (LFMC) plays a critical role in wildfire dynamics, but little is known about responses of LFMC to multivariate climate change, e.g., warming temperature, CO2 fertilization, and altered precipitation patterns, leading to a limited prediction ability of future wildfire risks. Here, we use a hydrodynamic demographic vegetation model to estimate LFMC dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. We parameterize the model based on observed shrub allometry and hydraulic traits and evaluate the model's accuracy through comparisons between observed and simulated LFMC of three plant functional types (PFTs) under current climate conditions. Moreover, we estimate the number of days per year of LFMC below 79 % (which is a critical threshold for wildfire danger rating of southern California chaparral shrubs) from 1960 to 2099 for each PFT and compare the number of days below the threshold for medium and high greenhouse gas emission scenarios (RCP4.5 and 8.5). We find that climate change could lead to more days per year (5.2 %–14.8 % increase) with LFMC below 79 % between the historical (1960–1999) and future (2080–2099) periods, implying an increase in wildfire danger for chaparral shrubs in southern California. Under the high greenhouse gas emission scenario during the dry season, we find that the future LFMC reductions mainly result from a warming temperature, which leads to 9.1 %–18.6 % reduction in LFMC. Lower precipitation in the spring leads to a 6.3 %–8.1 % reduction in LFMC. The combined impacts of warming and precipitation change on fire season length are equal to the additive impacts of warming and precipitation change individually. Our results show that the CO2 fertilization will mitigate fire risk by causing a 3.5 %–4.8 % increase in LFMC. Our results suggest that multivariate climate change could cause a significant net reduction in LFMC and thus exacerbate future wildfire danger in chaparral shrub systems.

Plant community composition influences carbon, water, and energy fluxes at regional to global scales. Vegetation demographic models (VDMs) allow investigation of the effects of changing climate and disturbance regimes on vegetation composition and fluxes. Such investigation requires that the models can accurately resolve these feedbacks to simulate realistic composition. Vegetation in VDMs is composed of plant functional types (PFTs), which are specified according to plant traits. Defining PFTs is challenging due to large variability in trait observations within and between plant types and a lack of understanding of model sensitivity to these traits. Here we present an approach for developing PFT parameterizations that are connected to the underlying ecological processes determining forest composition in the mixed-conifer forest of the Sierra Nevada of California, USA. We constrain multiple relative trait values between PFTs, as opposed to randomly sampling within the range of observations. An ensemble of PFT parameterizations are then filtered based on emergent forest properties meeting observation-based ecological criteria under alternate disturbance scenarios. A small ensemble of alternate PFT parameterizations is identified that produces plausible forest composition and demonstrates variability in response to disturbance frequency and regional environmental variation. Retaining multiple PFT parameterizations allows us to quantify the uncertainty in forest responses due to variability in trait observations. Vegetation composition is a key emergent outcome from VDMs and our methodology provides a foundation for robust PFT parameterization across ecosystems.

The surveying and mapping administrative competent departments in 31 provinces (autonomous regions and municipalities) have built provincial-level satellite navigation and positioning reference stations and data centers, and provided CORS services. This provides a good condition for exploring the application of geological hazard monitoring and early warning using Virtual Reference Station (VRS) service based on CORS. At present, the layout mode of "one point one reference station" is usually adopted, when GNSS is used for geological disaster monitoring and early warning. However, the high deployment cost of this plan limits its largescale promotion and application. Using the existing CORS service resources of natural resource system, this paper carried out the application experiment of virtual reference station in geological hazard monitoring application at Huanglongya geological hazard monitoring site in Shaanxi Province, and assessed the virtual reference station data quality, comparative analyzed the precision of static baseline processing results and GNSS real-time deformation monitoring results. The experimental results show that the overall quality of virtual reference station data is better than that of the monitoring station, and the accuracy of the static baseline calculation results is better than 1.0cm in the X direction, and better than 2.0cm in the Y direction and Z direction, which is similar to the static baseline calculation results formed by the physical reference station. The accuracy of the baseline results of real-time observation data calculation is better than 5mm in horizontal RMS and 15mm in vertical RMS. Therefore, it can be seen that the virtual reference station is feasible to be used as the reference station for geological disaster monitoring. In addition, the application experiment of network RTK real-time dynamic single epoch positioning mode is also carried out in geological hazard monitoring. The experimental results show that the RMS values of all three directions are ±3.7mm, ±9.2mm and ±5.0mm respectively, which meet the precision requirements of GNSS disaster monitoring. Therefore, it is also a feasible scheme for geological disaster monitoring and early warning.

Abstract Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

Lapping and polishing technology is an efficient processing method for wafer planarization processing. The structure of the fixed abrasive pad (FAP) is one of the most concerning issues in the research. The FAP structure affects the pressure distribution on the wafer surface, and the pressure distribution during processing has a significant influence on the wafer surface. Therefore, in this paper, a better pressure distribution is obtained by adjusting the angle of the spiral arrangement and the damping distribution of the damping layer of the FAP, thereby obtaining better processing quality. Based on the above theory, a new type of FAP, with coupling between the arrangement of the pellets and the damping regulation of the damping layer, was designed and optimized. The machining effects of different FAPs on the workpiece surface are compared in terms of material removal rate, material removal thickness, and surface roughness. The test results show that the workpiece material removal rate is higher than that of the traditional FAP when using the optimized FAP. The non-uniformity of the optimized FAP for that of material removal was 4.034 µm, which was lower than the traditional FAPs by 24.4 % and 17.6 %, respectively. The average surface roughness, Ra, of the optimized FAP is 0.21 µm, which is lower than 19.1 % and 12.5 % of the two traditional FAPs, respectively. Therefore, workpiece material removal and distribution are more uniform, and the surface quality of the workpiece is better when the optimized FAP processing is used. The test results prove that the optimized pellet arrangement and damping can achieve a better surface quality of the workpiece, which can meet the precision lapping process requirements for high-quality surfaces and large-scale production of brittle and hard materials such as sapphire.

A high-resolution air pollutant emission inventory for the Yangtze River Delta (YRD) region was updated for 2017 using emission factors and chemical speciation based mainly on local measurements in this study. The inventory included 424 non-methane volatile organic compounds (NMVOCs) and 43 fine particulate matter (PM2.5) species from 259 specific sources. The total emissions of SO2, NOx, CO, NMVOCs, PM10, PM2.5, and NH3 in the YRD region in 2017 were 1552, 3235, 38 507, 4875, 3770, 1597, and 2467 Gg, respectively. SO2 and CO emissions were mainly from boilers, accounting for 49 % and 73 % of the total. Mobile sources dominated NOx emissions, contributing 57 % of the total. NMVOC emissions, mainly from industrial sources, made up 61 % of the total. Dust sources accounted for 55 % and 28 % of PM10 and PM2.5 emissions, respectively. Agricultural sources accounted for 91 % of NH3 emissions. Major PM2.5 species were OC, Ca, Si, PSO4, and EC, accounting for 9.0 %, 7.0 %, 6.4 %, 4.6 %, and 4.3 % of total PM2.5 emissions, respectively. The main species of NMVOCs were aromatic hydrocarbons, making up 25.3 % of the total. Oxygenated volatile organic compounds (OVOCs) contributed 21.9 % of the total NMVOC emissions. Toluene had the highest comprehensive contribution to ozone (O3) and secondary organic aerosol (SOA) formation potentials, while other NMVOCs included 1,2,4-trimethylbenzene, m,p-xylene, propylene, ethene, o-xylene, and ethylbenzene. Industrial process and solvent-use sources were the main sources of O3 and SOA formation potential, followed by motor vehicles. Among industrial sources, chemical manufacturing, rubber and plastic manufacturing, appliance manufacturing, and textiles made significant contributions. This emission inventory should provide scientific guidance for future control of air pollutants in the YRD region of China.

ABSTRACT The objective of this study was to investigate the influence of plasma pathological changes before timed artificial insemination (TAI) on pregnancy of cows. The contents of estrogen (E2), progesterone (P4), glucose (Glu), selenium (Se), brain-derived neurotrophic factor (BDNF), and histamine (HIS) in plasma of 48 Holstein cows were measured before TAI. According to the estrus detection, the cows were divided into estrus (E) and anestrus (A) groups. After pregnancy testing at 28 d after TAI, two groups of E and A were divided into positive pregnancy of E group (EP+), negative pregnancy of E group (EP-), positive pregnancy of A group (AP+), and negative pregnancy of A group (AP-). The contents of E2, P4, Glu, Se, BDNF and hIS significantly differed among the four groups (P

In the oil-gas mixture transportation system of offshore oilfields, it is of great theoretical and practical significance to study the flow characteristics of the slug flow and its suppression or elimination. In this paper, the characteristics of the slug flow and microbubbles in a laboratory-scale rig are experimentally studied and analyzed by a multi-parameters measurement system including electrical resistance tomography (ERT), high-speed camera, and traditional pressure sensor. The suppression of the microbubbles on the slug flow formation is further investigated. Experimental results showed that the bubbles with different sizes from the microbubble generator have different aggregation and dispersion characteristics. The microbubbles can suppress the formation of the slug flow by increasing the liquid slug pressure and further affect the motion of the slug by enhancing the disturbance effect of the boundary layer, so as to achieve the suppression on slug flow.

Climate changes severely influence photosynthetic activity of plants. Studies on the combined effect of drought and heat stress on photosynthesis, pigments, and xanthophyll cycle particularly in alfalfa (Medicago sativa L.) are limited. Thus, this study investigated the combined effects of drought and heat stress in two alfalfa varieties ('Deqin' and 'Algonguin'). Results revealed that the values of the net photosynthetic rate demonstrated a significantly decreasing tendency under stresses. The effect of stresses on ribulose 1,5-bisphosphate carboxylase activity in both varieties showed a trend of first declining and then rising. Meanwhile, phosphoenolpyruvate carboxylase activity showed an increasing tendency. The intensification of stresses led to reduced photochemical efficiency and increased capability of thermal dissipation. A linear correlation was found between nonphotochemical quenching and xanthophyll cycle components. Thus, the change in carotenoids demonstrated an important role in photoprotection, the xanthophyll cycle was the main way of dissipation of excess energy under stress conditions in alfalfa.

Oxygen isotope ratios (δ18O) of tree-ring cellulose are a novel proxy for summer hydroclimate in monsoonal Asia. In central Japan, we collected 67 conifer wood samples, mainly Chamaecyparis obtusa, with ages encompassing the past 2600 years. The samples were taken from living trees, archeological wood, architectural wood, and buried logs. We analyzed stable isotope ratios of oxygen (δ18O) and hydrogen (δ2H) in tree-ring cellulose in these samples (more than 15 000 rings in total) without using a pooling method and constructed a statistically reliable tree-ring cellulose δ18O time series for the past 2500 years. However, there were distinct age trends and level offsets in the δ18O record, and cellulose δ18O values showed a gradual decrease as an individual tree matures. This suggested it is difficult to establish a cellulose δ18O chronology for low-frequency signals by simple averaging of all the δ18O time series data. In addition, there were opposite age trends in the cellulose δ2H, and δ2H gradually increased with tree age. There were clear positive correlations in the short-periodicity variations between δ18O and δ2H, probably indicating a common climate signal. A comparison of the δ18O and δ2H time series in individual trees with tree-ring width suggested that the opposite age trends of δ18O and δ2H are caused by temporal changes in the degree of post-photosynthetic isotope exchange with xylem water (physiological effect), accompanied by changes in stem growth rate that are influenced by human activity in the forests of central Japan. Based on the assumptions that cellulose δ18O and δ2H vary positively and negatively with constant proportional coefficients due to climatological and physiological effects, respectively, we solved simultaneous equations for the climatological and physiological components of variations in tree-ring cellulose δ18O and δ2H in order to remove the age trend. This enabled us to evaluate the climatic record from cellulose δ18O variations. The extracted climatological component in the cellulose δ18O for the past 2600 years in central Japan was well correlated with numerous instrumental, historical, and paleoclimatological records of past summer climate at various spatial and temporal scales. This indicates that integration of tree-ring cellulose δ18O and δ2H data is a promising method to reconstruct past summer climate variations on annual to millennial timescales, irrespective of the growth environment. However, analytical and statistical methods need to be improved for further development of this climate proxy.

Measurement-outliers caused by non-linear observation model or random disturbance will lead to the accuracy decline of a target tracking filter. This paper proposes a robust probability hypothesis density (PHD) filter to handle the measurement-outlier problem based on Student’s T Kalman (TK) filtering technique and Variational Bayesian (VB) method. First, the non-standard measurement noise is considered to follow the Student’s T distribution. Second, the TK filtering technique is employed to update the target states. Third, the posterior likelihood is updated by the VB approach. Simulation results show that the proposed method can reduce the optimal subpattern assignment (OSPA) error in the non-standard observation scenarios with measurement-outliers, compared with other typical multiple target tracking filters.

The structure of the lightweight honeycomb sandwich panel is complex. Thus, establishing an equivalent simplified model is indispensable to improve the efficiency of the dynamic analysis of honeycomb sandwich panels. In this paper, three commonly used dynamically equivalent modeling methods for honeycomb sandwich panel are studied: a dynamically equivalent method based on laminated plate theory, a single-layer plate equivalent method based on the theory of Hoff (1948), and an improved equivalent method based on Allen (1969). Using theoretical study, numerical simulations, and experiments, the applicability of these equivalent methods and the effect of design parameters on the dynamic characteristics are studied, and the optimal dynamically equivalent method for honeycomb sandwich panels is obtained.

Remote Sensing scene classification aims to identify semantic objects with similar characteristics from high resolution images. Even though existing methods have achieved satisfactory performance, the features used for classification modeling are still limited to some kinds of vector representation within a Euclidean space. As a result, their models are not robust to reflect the essential scene characteristics, hardly to promote classification accuracy higher. In this study, we propose a novel scene classification method based on the intrinsic mean on a Lie Group manifold. By introducing Lie Group machine learning into scene classification, the new method uses the geodesic distance on the Lie Group manifold, instead of Euclidean distance, solving the problem that non-euclidean space samples could not be calculated by Euclidean distance directly. The experiments show that our method produces satisfactory performance on two public and challenging remote sensing scene datasets, UC Merced and SIRI-WHU, respectively.

Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early- and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.

The 2015–2016 El Niño event ranks as one of the most severe on record in terms of the magnitude and extent of sea surface temperature (SST) anomalies generated in the tropical Pacific Ocean. Corresponding global impacts on the climate were expected to rival, or even surpass, those of the 1997–1998 severe El Niño event, which had SST anomalies that were similar in size. However, the 2015–2016 event failed to meet expectations for hydrologic change in many areas, including those expected to receive well above normal precipitation. To better understand how climate anomalies during an El Niño event impact soil moisture, we investigate changes in soil moisture in the humid tropics (between ±25∘) during the three most recent super El Niño events of 1982–1983, 1997–1998 and 2015–2016, using data from the Global Land Data Assimilation System (GLDAS). First, we use in situ soil moisture observations obtained from 16 sites across five continents to validate and bias-correct estimates from GLDAS (r2=0.54). Next, we apply a k-means cluster analysis to the soil moisture estimates during the El Niño mature phase, resulting in four groups of clustered data. The strongest and most consistent decreases in soil moisture occur in the Amazon basin and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. In addition, we compare changes in soil moisture to both precipitation and evapotranspiration, which showed a lack of agreement in the direction of change between these variables and soil moisture most prominently in the southern Amazon basin, the Sahel and mainland southeastern Asia. Our results can be used to improve estimates of spatiotemporal differences in El Niño impacts on soil moisture in tropical hydrology and ecosystem models at multiple scales.

Multiple observation data sets – Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.

Low temperature (LT) and low irradiation (LI) are common factors posing a great risk to plants. The study aimed to elucidate the effects of LT and LI and recovery on the photosynthetic apparatus, photoinhibition of PSII, and reactive oxygen metabolism of strawberry seedlings. The results showed that strawberry growth slowed down or even stopped and total chlorophyll content, stomatal conductance, net photosynthetic rate, and maximal quantum yield of PSII photochemistry decreased, while intercellular CO2 concentration increased under LI, LT, and combined stress (LL). Additionally, JIP-test showed that compared to LI or LT stress, LL-stressed plants had lower quantum yields and efficiencies and functional antenna size, and higher reaction center activity. Besides, the contents of hydrogen peroxide and malondialdehyde increased, while the activity of superoxide dismutase, peroxidase, and catalase were significantly inhibited compared with the control. After the stress was relieved, the photosynthesis of LL-stressed plants recovered poorly.