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Infrared-triggered cameras serve as essential tools for wildlife resource surveys, allowing camera traps to capture wildlife activity while minimizing the impact on the ecosystem. Traditional monitoring methods heavily rely on human resources for visual discrimination, which is inefficient and susceptible to environmental influences. Therefore, this paper focuses on Shennongjia National Park as a case study to address the limitations of traditional approaches. We constructed a Shennongjia wildlife object detection dataset using video data from the fixed-erected infrared cameras and proposed a supervised learning-based automatic monitoring method for golden monkeys, aiming to achieve intelligent target detection. With these processing results, we were able to capture golden monkeys' tracks and conduct statistical analysis of their life range and distribution characteristics. Through the integration of sensors and deep learning techniques, we developed a golden monkeys detection monitoring system to visualize the monitoring results and assess the spatial and temporal distribution characteristics of golden monkeys’ activities.

This work is devoted to the development of a new model for the non-atomizing sprinkler irrigation jet, for calculating the trajectories and landing positions of water droplets. The novelty of the proposed model is that the secondary breakup of the droplets can be calculated during the spraying process. For irrigation jet with a second wind-induced breakup regime, the model is optimized based on the ballistic theory by considering the secondary breakup of droplets and the jet breakup length. The wave-breaking model is used to determine the secondary breakup of the droplets. The output of this model is the water application rate that is calculated by using the cumulative volume of droplets along the radial spraying direction. A comparison of the results obtained using the proposed model with experimental data is conducted to verify the accuracy and reliability of the proposed model. The results show a good agreement of the peak water application rate between the optimized model and the experimental data, with an average error ranging within 6%. The droplets in the front spraying area usually have a diameter of 0-2 mm. This is computed by using the droplet secondary breakup sub-model, resulting in a considerably improved accuracy of the optimized model in the prediction of the water application rate of a sprinkler.

Accurate detection and automatic processing of earthquake-damaged regions is essential for effective rescue and post-disaster reconstruction. In this study, we proposed a Combined Super-pixel Segmentation and AlexNet Detection approach (CSSAD) for automatically extracting damaged regions from post-earthquake high-resolution images. Simple Linear Iterative Clustering (SLIC) algorithm was used to segment the high resolution images to obtain more homogeneous geo-objects. Multiscale samples database, which took the different scale effect of damaged regions into account, was constructed based on the geometric centre of each super-pixel. AlexNet, which achieved the automatic extraction of high-level features and accurate identification of target geo-objects, was used to detect the damaged regions. To enhance the localization accuracy, the output of AlexNet was further refined using super-pixel segmentations and masked out of shadow and vegetation. Compared with traditional method, the proposed approach effectively reduces the false and missed detection ratio at least 10 percent.

The square conservation law is implemented in atmospheric dynamic cores on latitude–longitude grids, but it is rarely implemented on quasi-uniform grids, given the difficulty involved in constructing anti-symmetrical spatial discrete operators on these grids. Increasingly more models are being developed on quasi-uniform grids, such as arbitrarily structured C-grids. Thuburn–Ringler–Skamarock–Klemp (TRiSK) is a shallow water dynamic core on an arbitrarily structured C-grid. The spatial discrete operator of TRiSK is able to naturally maintain the conservation properties of total mass and total absolute vorticity and conserving total energy with time truncation error; the first two integral invariants are exactly conserved during integration, but the total energy dissipates when using the dissipative temporal integration schemes, i.e., Runge–Kutta (RK). The method of strictly conserving the total energy simultaneously, which means conserving energy in the round-off error over the entire temporal integration period, uses both an anti-symmetrical spatial discrete operator and a square conservative temporal integration scheme. In this study, we demonstrate that square conservation is equivalent to energy conservation in both a continuous shallow water system and a discrete shallow water system of TRiSK. After that, we attempt to extend the square conservation law to the TRiSK framework. To overcome the challenge of constructing an anti-symmetrical spatial discrete operator, we unify the unit of evolution variables of shallow water equations using the Institute of Atmospheric Physics (IAP) transformation, and the temporal derivatives of new evolution variables can be expressed by a combination of temporal derivatives of original evolution variables, which means the square conservative spatial discrete operator can be obtain by using original spatial discrete operators in TRiSK. Using the square conservative Runge–Kutta scheme, the total energy is completely conserved, and there is no influence on the properties of conserving total mass and total absolute vorticity. In the standard shallow water numerical test, the square conservative scheme not only helps maintain total conservation of the three integral invariants but also creates less simulation error norms.

Our objective was to investigate the distributions of six single nucleotide polymorphisms (SNPs) MS4A2 E237G, MS4A2 C-109T, ADRB2 R16G, IL4RA I75V, IL4 C-590T, and IL13 C1923T in Mauritian Indian and Chinese Han children with asthma. This case-control association study enrolled 382 unrelated Mauritian Indian children, 193 with asthma and 189 healthy controls, and 384 unrelated Chinese Han children, 192 with asthma and 192 healthy controls. The SNP loci were genotyped using polymerase chain reaction (PCR)-restriction fragment length polymorphism for the Chinese Han samples and TaqMan real-time quantitative PCR for the Mauritian Indian samples. In the Mauritian Indian children, there was a significant difference in the distribution of IL13 C1923T between the asthma and control groups (P=0.033). The frequency of IL13 C1923T T/T in the Mauritian Indian asthma group was significantly higher than in the control group [odds ratio (OR)=2.119, 95% confidence interval=1.048-4.285]. The Chinese Han children with asthma had significantly higher frequencies of MS4A2 C-109T T/T (OR=1.961, P=0.001) and ADRB2 R16G A/A (OR=2.575, P=0.000) than the control group. The IL13 C1923T locus predisposed to asthma in Mauritian Indian children, which represents an ethnic difference from the Chinese Han population. The MS4A2 C-109T T/T and ADRB2 R16G A/A genotypes were associated with asthma in the Chinese Han children.

Automatic water-body extraction from remote sense images is a challenging problem. Using GIS data to update and extract waterbody is an old but active topic. However, automatic registration and change detection of the two data sets often presents difficulties. In this paper, a novel automatic water-body extraction method is proposed. The core idea is to integrate image segmentation, image registration and change detection with GIS data as a whole processing. A new iterative segmentation and registration strategy is proposed. Here, the so-called visual attention model and level set segmentation algorithm is introduced to find interesting areas with suspected water-bodies and to obtain initial objects contours. And an improved shape curve similarity (ISCS) is presented to match image segmentation objects and GIS water-bodies with overall spatial constraints. Furthermore buffer based change detection algorithm is designed to obtain unchanged water-bodies and SVM classifier is used to verify changed ones. Experiment results show that proposed method was effective in rapid extraction and change detection of water-bodies.

Source contributions to ambient PM10 (particles with an aerodynamic diameter of 10 μm or less) in Beijing, China were determined with positive matrix factorization (PMF) based on ambient PM10 composition data including concentrations of organic carbon (OC), elemental carbon (EC), ions and metal elements, which were simultaneously obtained at six sites through January, April, July and October in 2004. Results from PMF indicated that seven major sources of ambient PM10 were urban fugitive dust, crustal soil, coal combustion, secondary sulfate, secondary nitrate, biomass burning with municipal incineration, and vehicle emission, respectively. In paticular, urban fugitive dust and crustal soil as two types of dust sources with similar chemical characteristics were differentiated by PMF. Urban fugitive dust contributed the most, accounting for 34.4% of total PM10 mass on an annual basis, with relatively high contributions in all four months, and even covered 50% in April. It also showed higher contributions in southwestern and southeastern areas than in central urban areas. Coal combustion was found to be the primary contributor in January, showing higher contributions in urban areas than in suburban areas with seasonal variation peaking in winter, which accounted for 15.5% of the annual average PM10 concentration. Secondary sulfate and secondary nitrate combined as the largest contributor to PM10 in July and October, with strong seasonal variation peaking in summer, accounting for 38.8% and 31.5% of the total PM10 mass in July and October, respectively. Biomass burning with municipal incineration contributions were found in all four months and accounted for 9.8% of the annual average PM10 mass concentration, with obviously higher contribution in October than in other months. Incineration sources were probably located in southwestern Beijing. Contribution from vehicle emission accounted for 5.0% and exhibited no significant seasonal variation. In sum, PM10 source contributions in Beijing showed not only significant seasonal variations but also spatial differences.

Accurate and timely global evapotranspiration (ET) data are crucial for agriculture, water resource management, and drought forecasting. Although numerous satellite-based ET products are available, few offer near-real-time data. For instance, products like NASA's ECOsystem Spaceborne Thermal Radiometer Experiment mounted on the International Space Station (ECOSTRESS) and MOD16 face challenges such as uneven coverage and delays exceeding 1 week in data availability. In this study, we refined the Variation of the Standard Evapotranspiration Algorithm (VISEA) by fully integrating satellite-based data, e.g., European Centre for Medium-Range Weather Forecasts ERA5-Land shortwave radiation (which includes satellite remote sensing data within its assimilation system) and MODIS land surface data (which include surface reflectance, temperature and/or emissivity, land cover, vegetation indices, and albedo as inputs). This enables VISEA to provide near-real-time global daily ET estimates with a maximum delay of 1 week at a resolution of 0.05°. Its accuracy was assessed globally using observation data from 149 flux towers across 12 land cover types and comparing them with five other satellite-based ET products and Global Precipitation Climatology Centre (GPCC) data. The results indicate that VISEA provides accurate ET estimates that are comparable to existing products, achieving a mean correlation coefficient (R) of about 0.6 and an RMSE of 1.4 mm d−1. Furthermore, we demonstrated VISEA's utility in drought monitoring during a drought event in the Yangtze River basin in 2022 in which ET changes correlated with precipitation. The near-real-time capability of VISEA is, thus, especially valuable in meteorological and hydrological applications for coordinating drought relief efforts. The VISEA ET dataset is available at https://doi.org/10.11888/Terre.tpdc.300782 (Huang, 2023a).

The total freeboard, which is the ice layer above water level and includes the snow thickness, is needed to retrieve the ice thickness and ice surface topography. Single-pass interferometric synthetic aperture radar (InSAR) allows for the generation of digital elevation models (DEMs) over the drifting sea ice. However, accurate sea ice DEMs (i.e., the total freeboard) derived from InSAR are impeded due to variation in the penetration of the radar signals into the snow and ice layers. This research introduces a novel methodology for retrieving sea ice DEMs using dual-polarization interferometric SAR images, considering the variation in radar penetration bias across multiple ice types. The accuracy of the method is verified through photogrammetric measurements, demonstrating that the derived DEM has a root-mean-square error of 0.26 m over a 200 km × 19 km area. The method is further applied to broader regions in the Weddell Sea and the Ross Sea, offering new insights into the regional variations of the sea ice topography in the Antarctic. We also characterize the non-Gaussian statistical behavior of the total freeboard using log-normal and exponential-normal distributions. The results suggest that the exponential-normal distribution is superior in the thicker-sea-ice region (average total freeboard > 0.5 m), whereas the two distributions exhibit similar performance in the thinner-ice region (average total freeboard < 0.5 m). These findings offer an in-depth representation of the total freeboard and roughness in the Weddell and Ross seas. The novel methodology introduced here can be conducted on time series data to comprehend the dynamics of the sea ice, including its growth and deformation.

The rapid changes occurring in the polar regions require an improved understanding of the processes that are driving these changes. At the same time, increased human activities such as marine navigation, resource exploitation, aviation, commercial fishing, and tourism require reliable and relevant weather information. One of the primary goals of the World Meteorological Organization's Year of Polar Prediction (YOPP) project is to improve the accuracy of numerical weather prediction (NWP) at high latitudes. During YOPP, two Canadian “supersites” were commissioned and equipped with new ground-based instruments for enhanced meteorological and system process observations. Additional pre-existing supersites in Canada, the United States, Norway, Finland, and Russia also provided data from ongoing long-term observing programs. These supersites collected a wealth of observations that are well suited to address YOPP objectives. In order to increase data useability and station interoperability, novel Merged Observatory Data Files (MODFs) were created for the seven supersites over two Special Observing Periods (February to March 2018 and July to September 2018). All observations collected at the supersites were compiled into this standardized NetCDF MODF format, simplifying the process of conducting pan-Arctic NWP verification and process evaluation studies. This paper describes the seven Arctic YOPP supersites, their instrumentation, data collection and processing methods, the novel MODF format, and examples of the observations contained therein. MODFs comprise the observational contribution to the model intercomparison effort, termed YOPP site Model Intercomparison Project (YOPPsiteMIP). All YOPPsiteMIP MODFs are publicly accessible via the YOPP Data Portal (Whitehorse: https://doi.org/10.21343/a33e-j150, Huang et al., 2023a; Iqaluit: https://doi.org/10.21343/yrnf-ck57, Huang et al., 2023b; Sodankylä: https://doi.org/10.21343/m16p-pq17, O'Connor, 2023; Utqiaġvik: https://doi.org/10.21343/a2dx-nq55, Akish and Morris, 2023c; Tiksi: https://doi.org/10.21343/5bwn-w881, Akish and Morris, 2023b; Ny-Ålesund: https://doi.org/10.21343/y89m-6393, Holt, 2023; and Eureka: https://doi.org/10.21343/r85j-tc61, Akish and Morris, 2023a), which is hosted by MET Norway, with corresponding output from NWP models.

Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).

TP53 has been shown to be involved in lactation in cattle. However, the role of TP53 in buffalo lactation remains unknown. To this end, we isolated and identified the complete coding sequence (CDS) of the TP53 gene from the buffalo mammary gland and further analyzed its molecular characteristics, function, tissue differential expression, and single-nucleotide polymorphism (SNP). A transcript of this gene was cloned with a CDS length of 1161 bp, encoding a protein consisting of 386 amino acid residues. Bioinformatics analysis showed that buffalo TP53 CDS and the physicochemical characteristics, conserved domains, structure, and function of its encoded protein are highly similar to those of other species in Bovidae. The buffalo TP53 protein contains an N-terminal activation domain, a DNA-binding domain, and a tetrameric domain, and it plays a functional role in the nucleus. TP53 was found to express in all 11 detected buffalo tissues, and its expression in the heart, kidney, brain, muscle, and rumen during lactation was significantly higher than that during non-lactation (p), while in the liver, lung, and mammary gland, its expression was the opposite (p). Interference experiments in buffalo mammary epithelial cells (BuMECs) showed that TP53 inhibits the expression of genes related to milk protein and milk fat synthesis through the PI3K–AKT–mTOR pathway. A synonymous nucleotide substitution (c.204C > T) was found in the TP53 CDS of river buffalo, which is the CC homozygote in swamp buffalo. The results indicate that the TP53 gene is involved in buffalo lactation by negatively regulating the synthesis of milk protein and milk fat.

Investigating the global time-varying gravity field mainly depends on GRACE/GRACE-FO gravity data. However, satellite gravity data exhibit low spatial resolution and signal distortion. Satellite altimetry is an important technique for observing the global ocean and provides many consecutive years of data, which enables the study of high-resolution marine gravity variations. This study aims to construct a high-resolution marine gravity change rate (MGCR) model using multi-satellite altimetry data. Initially, multi-satellite altimetry data and ocean temperature–salinity data from 1993 to 2019 are utilized to estimate the altimetry sea level change rate (SLCR) and steric SLCR, respectively. Subsequently, the mass-term SLCR is calculated. Finally, based on the mass-term SLCR, the global MGCR model on 5′ × 5′ grids (SDUST2020MGCR) is constructed by applying the spherical harmonic function method and mass load theory. Comparisons and analyses are conducted between SDUST2020MGCR and GRACE2020MGCR resolved from GRACE/GRACE-FO gravity data. The spatial distribution characteristics of SDUST2020MGCR and GRACE2020MGCR are similar in the sea areas where gravity changes significantly, such as the eastern seas of Japan, the western seas of the Nicobar Islands, and the southern seas of Greenland. The statistical mean values of SDUST2020MGCR and GRACE2020MGCR in global and local oceans are all positive, indicating that MGCR is rising. Nonetheless, differences in spatial distribution and statistical results exist between SDUST2020MGCR and GRACE2020MGCR, primarily attributable to spatial resolution disparities among altimetry data, ocean temperature–salinity data, and GRACE/GRACE-FO data. Compared with GRACE2020MGCR, SDUST2020MGCR has higher spatial resolution and excludes stripe noise and leakage errors. The high-resolution MGCR model constructed using altimetry data can reflect the long-term marine gravity change in more detail, which is helpful in studying seawater mass migration and its associated geophysical processes. The SDUST2020MGCR model data are available at https://doi.org/10.5281/zenodo.10701641 (Zhu et al., 2024).

Various ground-based observing techniques provide precipitable water vapor (PWV) products with different spatial resolutions. To effectively integrate these products, especially in terms of vertical orientation, spatial interpolation is essential. In this context, we have developed a model to characterize PWV variation with altitude over our study area. Our model, known as RF-PWV (a PWV vertical correction grid model with a 1° × 1° resolution), is constructed using random forest based on the relationship between the differences in different pressure level PWV data from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) monthly average hourly data and corresponding differences in their height differences over time. When validated against 1 h ERA5 PWV profiles, RF-PWV exhibits a 99.84 % reduction in bias and a 63.41 % decrease in the RMSE compared with the most recent model, C-PWVC1. Furthermore, when validated against radiosonde data, RF-PWV shows a 96.36 % reduction in bias and a 5 % decrease in the RMSE compared with C-PWVC1. Additionally, RF-PWV outperforms C-PWVC1 in terms of resistance to seasonal and height difference interference. The model eliminates the need for meteorological parameters, allowing for high-precision PWV vertical correction by inputting only time and height differences. Consequently, RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.

Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Low rare earth content and high intrinsic magnetic properties make SmFe12-based permanent magnets a potential candidate for NdFeB-based magnets in medium to high temperature environments. However, the easy formation of multiple ferromagnetic intergranular phases limits the preparation of high coercivity magnets. Composition optimization is an indispensable measure for the realization of high-performance magnets. In this work, off-stoichiometric composition design is employed to prepare SmFe12-based alloys. Ti is first introduced to inhibit the precipitation of α-Fe phase, obtaining a high coercivity of 484 kA/m, while the ribbon undergoes a reduction of remanence significantly. After regulating Fe–Co content to 11.5, the remanence recoveries from 0.51 to 0.65 T with a slight increase in coercivity (484–493 kA/m). Finally, Fe content is optimized to further improve magnetic properties, with both coercivity and remanence are improved simultaneously, reaching 509 kA/m and 0.70 T, respectively. Phase evolution, microstructure and their relationship with magnetic properties are investigated in detail. Micromagnetic simulation shows that the increased domain wall pinning effect induced by the refinement of grain size is the main reason for high coercivity. This work provides a feasible compositional design method for the preparation of high performance SmFe12-based alloys.