Search Results (133)

The Triple-angle Ionospheric PhotoMeter (Tri-IPM), an airglow and aurora monitoring payload onboard the Fengyun-3E (FY-3E) satellite, is designed for high-sensitivity observations of far-ultraviolet airglow during twilight from the ionosphere–thermosphere system. This compact, nadir-viewing instrument features three probes (A, B, and C) oriented at 0°, −30°, and 30° relative to the nadir direction, enabling multiangle detection of OI 135.6 nm and N₂ Lyman–Birge–Hopfield (LBH) band (147.5–162.5 nm) emissions. With a spatial resolution of ~30 km × 14 km and a responsivity exceeding 2 counts/s/R, the Tri-IPM achieves high-precision measurements while maintaining a red-leak suppression ratio of ~10⁹ to minimize spectral contamination. This paper presents the design principles, ground calibration, and preliminary on-orbit performance of the Tri-IPM. On-orbit tests demonstrate excellent agreement between the observed airglow radiances, their spatial distributions, and the solar zenith angle dependencies of the theoretical models. Furthermore, the results exhibit strong consistency with observations from the Global-scale Observations of the Limb and Disk (GOLD) mission, validating the instrument’s reliability. By providing high-sensitivity, high-resolution global observations of far-ultraviolet (FUV) twilight airglow, the Tri-IPM advances research on ionospheric–thermospheric dynamics and enhances space weather monitoring capabilities. Its integrated on-orbit calibration ensures long-term data accuracy, making it a valuable tool for both scientific studies and operational space environment surveillance. Full article

Deep convection strongly influences regional water cycles over the Tibetan Plateau (TP), often referred to as the “Asian Water Tower.” Using FY-2E thundercloud observations, we examined the deep convection characteristics over the central TP. Deep convective storms over the TP exhibit pronounced spatiotemporal heterogeneity. The frequency distribution of storm areas follows an exponential pattern in all seasons, and the cloud-top black body temperature (TBB) distribution is negatively skewed, with values concentrated between −40 and −36 °C. Deep convection is most active in summer, with storms that are larger and have colder cloud tops. In spring, storms are less frequent but tend to cover larger areas, whereas autumn is dominated by small- to medium-sized systems. Spatially, the southeastern and southwestern TP are high-frequency centers, with storm occurrence 2–3 times higher than in the northern TP. Associations between deep-convection properties and precipitation vary by season and region. In summer, storm-related precipitation is primarily linked to large storm areas, whereas in autumn it is more strongly associated with storms with lower TBB. In the southwestern TP, precipitation intensity is more strongly related to TBB, whereas in the northwestern TP, it is more sensitive to storm area. Topographic slope also modulates both precipitation and storm properties. Most storm precipitation occurs over slopes ≤14°, and heavy precipitation shows a bimodal dependence on slope, with peaks at 3–4° and 11–13°. Gentle slopes favor storm growth and horizontal expansion; as the slope increases, mean TBB increases, and deep convection weakens. Full article

The objective of this study was to compare the proportion of explained genetic variance (EXGV) and the reliability of genomic breeding values (GBVs) predictions for milk yield (MY), fat yield (FY), protein yield (PY) fat percentage (FP), protein percentage (PP), and somatic cell score (SCS) in Holstein cattle. Three types of genomic information were evaluated. (a) SNP-ALL: this analysis included 88,911 single nucleotide polymorphisms (SNP) from 8290 animals. (b) HAP-PSEUDOSNP: haplotypes, defined based on high linkage disequilibrium (LD, r² ≥ 0.80) between SNPs, which were encoded as pseudo-SNPs, with a total of 35,552 pseudo-SNPs and 8331 animals included. (c) SNP-HAP: analysis using only individual SNPs included in the haplotypes (without recoding); for this analysis, 33,010 SNPs and 8192 individuals were retained. All analyses were conducted using the single-step genome-wide association study method implemented in the BLUPF90 software package. The results showed that the inclusion of SNPs with high LD (SNP-HAP) increases the reliability of GBVs’ predictions compared to the SNP-ALL analysis; average reliability increased between 0.05 and 0.11. Moreover, the SNP-HAP analysis resulted in a twofold increase in the EXGV for all traits, likely due to increased estimates of individual marker effects compared to the SNP-ALL analysis. Full article

New-generation geostationary meteorological satellite radiometric imagers widely employ multi-column redundant long-linear-array detectors, for which the Best Detector Selection (BDS) strategy is crucial for enhancing the quality of remote sensing data. Addressing the limitation of current BDS methods that often rely on a single metric and thus fail to fully exploit the detector’s comprehensive performance, this paper proposes a detector evaluation method based on a modified Z-score. This method systematically categorizes detector metrics into three types: positive, negative, and uniformity. It introduces, for the first time, spectral response deviation (SRD) as an effective quantitative measure for the Spectral Response Function (SRF) and employs a robust normalization strategy using the Interquartile Range (IQR) instead of standard deviation, enabling multi-dimensional detector evaluation and selection. Validation using laboratory data from the FY-4C/AGRI long-wave infrared band demonstrates that, compared to traditional single-metric optimization strategies, the best detectors selected by our method show significant improvement across multiple performance indicators, markedly enhancing both data quality and overall system performance. The proposed method features low computational complexity and strong adaptability, supporting on-orbit real-time detector optimization and dynamic updates, thereby providing reliable technical support for high-quality processing of remote sensing data from geostationary meteorological satellites. Full article

To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms with a data-driven strategy by introducing a GeoAI framework. Ensemble learning methods (XGBoost, LightGBM, and Random Forest) were utilized to process a comprehensive set of predictors, integrating dynamic surface features derived from FY-4 satellite’s high-frequency observation data. The data comprised surface thermal regime metrics, specifically the daily maximum land surface temperature (LST_max) and its diurnal range (LST_{max_min}), along with vegetation indices including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Further, topographic attributes derived from a digital elevation model (DEM) were incorporated, such as slope, aspect, the terrain ruggedness index (TRI), and the topographic position index (TPI). The approach uniquely capitalized on the temporal resolution of geostationary data to capture the diurnal land surface dynamics crucial for bias correction. The proposed models not only enhanced temperature data quality but also achieved impressive accuracy. Across China, the root mean square error (RMSE) was reduced to 1.04 °C, mean absolute error (MAE) to 0.53 °C, and accuracy (ACC) to 0.97. Additionally, the most notable improvement was that the RMSE decreased by nearly 50% (from 2.17 °C to 1.11 °C), MAE dropped from 1.48 °C to 0.80 °C, and ACC increased from 0.72 to 0.96 in the southwestern region of China. The corrected rice heat stress data (2020–2023) indicated that significant negative correlations exist between yield loss and various heat stress metrics in the severely affected middle and lower Yangtze River region. The research confirms that embedding geostationary meteorological satellites within a GeoAI framework can effectively enhance the precision of agricultural weather monitoring and related impact assessments. Full article

Tomato (Solanum lycopersicum) is the most important vegetable crop globally; however, its production is often hindered by soil-borne biotic and abiotic stresses. The use of rootstocks provides an effective strategy to mitigate these soil-related challenges. Hence, the development of new rootstock cultivars remains crucial to meet the demands of rapidly changing environmental conditions. Wild tomato species represent valuable genetic resources for rootstock improvement and are increasingly utilized in rootstock breeding programs. Nevertheless, the genetic mechanisms, particularly quantitative trait loci (QTL), underlying rootstock–scion interaction, remain poorly understood. In this study, 38 introgression lines (ILs) derived from S. lycopersicoides were used as rootstock and grafted with the commercial cultivar ‘Torry F₁’ to evaluate their effects on morphological and fruit quality traits under greenhouse conditions. The evaluations included assessments of morphological and fruit quality traits for QTL analysis. A total of 19 QTLs were identified, associated with 11 traits such as yield, antioxidant capacity, flavonoid content, and fruit color parameters (L*, a*, b*, C*, h°), with the phenotypic variance explained ranging from 12% to 61%. Of these QTLs, seven favorable alleles originated from S. lycopersicoides, notably including a major yield-associated locus (Fy5.1). In addition, the identification of a QTL for scion stem thickness (Tsc3.1) highlights the genetic contribution of the rootstock to scion development. This study represents the first evaluation of the rootstock potential of S. lycopersicoides ILs and provides novel insights into the genetic basis of rootstock–scion interaction in tomato. The identified QTLs offer valuable information for future breeding efforts aimed at developing improved rootstock cultivars for sustainable tomato production. Full article

Microwave Radiation Imagers (MWRIs) onboard the FY-3B, FY-3C, and FY-3D satellites are the primary sensors for sea surface temperature (SST) observation. Benefiting from the resolution of several key calibration issues in brightness temperature products, MWRI SST records spanning more than a decade have been reprocessed. In this study, these reprocessed SST products are evaluated using direct comparison and the extended triple collocation (ETC) method, along with additional error analyses. Compared with iQuam SST, the reprocessed MWRI SST products from the three satellites show total root mean square errors (RMSEs) of 0.80–0.82 °C and total biases of −0.12 °C to 0.00 °C. ETC analyses based on MWRI, ERA5, and Argo SSTs indicate random errors of 0.76–0.78 °C. Furthermore, the reprocessed MWRI SST products demonstrate temporal stability and exhibit minimal crosstalk effects from sea surface wind speed, columnar water vapor, and columnar cloud liquid water in SST retrievals. Compared with previous versions, the reprocessed products show significant improvements, with consistent performance across FY-3B, FY-3C, and FY-3D. However, differences in SST observations due to the varying local times of the ascending nodes among the three satellites should be corrected in practical applications. Full article

The crystal structure of kuliokite-(Y), Y₄Al(SiO₄)₂(OH)₂F₅, has been re-investigated using the material from the type locality the Ploskaya Mt, Kola peninsula, Russian Arctic. It has been shown that in contrast to previous studies, the mineral is monoclinic, Im, with a = 4.3213(1), b = 14.8123(6), c = 8.6857(3) Å, β = 102.872(4)°, and V = 541.99(3) ų. The crystal structure was solved and refined to R₁ = 0.030 on the basis of 3202 unique observed reflections. The average chemical composition determined by electron microprobe analysis is (Y_2.96Yb_0.49Er_0.27Dy_0.13Tm_0.07Lu_0.05Ho_0.05Gd_0.01Ca_0.01)_Σ4.04Al_0.92Si_2.04O₈-[(OH)_2.61F_4.42]_Σ7.03; the idealized formula is (Y,Yb,Er)₄Al[SiO₄]₂(OH)_2.5F_4.5. The crystal structure of kuliokite-(Y) contains two symmetrically independent Y sites, Y1 and Y2, coordinated by eight and seven X anions, respectively (X = O, F). The coordination polyhedra can be described as a distorted square antiprism and a distorted pentagonal bipyramid, respectively. The refinement of site occupancies indicated that the mineral represents a rare case of HREE fractionation among two cation sites driven by their coordination numbers and geometry. In agreement with the lanthanide contraction, HREEs are selectively incorporated into the Y2 site with a smaller coordination number and tighter coordination environment. The strongest building unit of the structure is the [AlX₂(SiO₄)₂] chain of corner-sharing AlX₆ octahedra and SiO₄ tetrahedra running along the a axis. The chains have their planes oriented parallel to (001). The Y atoms are located in between the chains, along with the F⁻ and (OH)⁻ anions, providing the three-dimensional integrity of the crystal structure. Each F⁻ anion is coordinated by three Y³⁺ cations to form planar (FY₃)⁸⁺ triangles parallel to the (010) plane. The triangles share common edges to form [F₂Y₂]⁴⁺ chains parallel to the a axis. The analysis of second-neighbor coordination of Y sites allowed us to identify the structural topology of kuliokite-(Y) as the only case of the skd network in inorganic compounds, previously known in molecular structures only. The variety of anionic content in the mineral allows us to identify the potential existence of two other mineral species that can tentatively be named ‘fluorokuliokite-(Y)’ and ‘hydroxykuliokite-(Y)’. Full article

Clouds cover nearly two-thirds of Earth’s surface, making reliable cloud mask data essential for remote sensing applications and atmospheric research. This study develops a TrAdaBoost transfer learning framework that integrates active CALIOP and passive MODIS observations to enable unified, high-accuracy cloud detection across FY-4A/AGRI, FY-4B/AGRI, and Himawari-8/9 AHI sensors. The proposed TrAdaBoost Cloud Mask algorithm (TCM) achieves robust performance in dual validations with CALIPSO VFM and MOD35/MYD35, attaining a hit rate (HR) above 0.85 and a cloudy probability of detection (${POD}_{cld})$ exceeding 0.89. Relative to official products, TCM consistently delivers higher accuracy, with the most pronounced gains on FY-4A/AGRI. SHAP interpretability analysis highlights that 0.47 μm albedo, 10.8/10.4 μm and 12.0/12.4 μm brightness temperatures and geometric factors such as solar zenith angles (SZA) and satellite zenith angles (VZA) are key contributors influencing cloud detection. Multidimensional consistency assessments further indicate strong inter-sensor agreement under diverse SZA and land cover conditions, underscoring the stability and generalizability of TCM. These results provide a robust foundation for the advancement of multi-source satellite cloud mask algorithms and the development of cloud data products integrated. Full article

Background: Today, in Western countries, patients with allo-antibodies to high-frequency antigens or with complex antibody mixtures represent one of the most significant challenges in transfusion medicine. Another important aspect is the prevention of allo-immunization of patients who lack high-frequency antigens. In these conditions, the availability of a bank of a rare red blood cell group, supported by a database of donors subjected to extensive erythrocyte typing (preferably using erythrogenomic study), can constitute a resource of great value. Materials and Methods: Repeat Caucasian blood donors of group A or O, with selected Rh phenotypes (CCDee, ccDEE, ccdee, ccDee), aged under 52 years, were considered for typing. Moreover, we selected all non-Caucasian repeat blood donors for typing. For extended phenotyping and genotyping we adopted commercial methods supplied by Grfols and Werfen, respectively. For cryopreservation, we selected a high glycerol method in −80 °C electric freezer; blood unit processing was performed using a Haemonetics ACP 215 automated cell processor with close circuit devices. Results: We considered the five patients as follows: PA was massively transfused for a road trauma, developed multiple allo-antibodies (anti-D, anti-k), and required compatible blood units for an elective cardiac surgery; PB was a pregnant woman with anti-Coa (a high frequency antigen) monitored during pregnancy and in which it was necessary to proceed with the transfusion of the newborn; PC was a poly-transfused patient with myelo dysplastic syndrome who developed multiple allo-antibodies (anti-k, anti-CW, anti-Lea) and required continuous supportive therapy, including the procurement of compatible units and the implementation of therapeutic actions in an attempt to reduce the transfusion requirement using luspatercept; PD was a patient with sickle cell disease. They had a Fy (null) genotype, making it very difficult to find compatible units; and PE was interesting for the complexity of the immunohematological and erythrogenomic study performed to characterize a recipient with a rare phenotype and thus allow the transfusion of compatible units, preventing allo-immunization. Discussion: In this report, we have maintained a narrative approach. Starting with five patients representing as many clinical situations as possible, we have illustrated the approach followed for the immune-hematological study and the choices made to try to guarantee effective and safe transfusion therapy. Full article

To investigate how sweating–drying processing affects the components, antioxidant activity, and hepatoprotective mechanisms of Eucommia ulmoides (EUB) against acute liver injury (ALI), this study constructed a “processing–active components–ALI targets” network. Eight processed EUB samples were analyzed using HPLC fingerprinting, multi-assay antioxidant tests (DPPH/ABTS·+/pyrogallol), network pharmacology, and molecular docking. Sweating–drying significantly altered EUB’s chemical profile, with HPLC fingerprint similarities ranging from 0.715 to 1.000, the lowest being for FG4 (40 °C dried after sweating) and FD (freeze-dried after sweating). Key components (chlorogenic acid (CA), pinoresinol diglucoside (PDG), aucubin (AU), geniposidic acid (GPA)) varied: XS (sun-dried) had the highest CA/PDG, while FG4 showed increased AU/GPA. FY (shade-dried after sweating) exhibited the strongest free radical scavenging (DPPH/ABTS·+/pyrogallol IC₅₀ = 0.828, 0.134, 14.200 mg/mL), which correlated with CA/PDG/liriodendrin (PD) synergy. Network pharmacology identified 205 EUB-ALI intersection targets (core: TNF, PTGS2, GAPDH) and the AGE-RAGE pathway; molecular docking confirmed strong CA/PDG binding to GAPDH/PTGS2. This study clarifies how processing regulates EUB’s components and their links to antioxidant and hepatoprotective effects, providing scientific support for EUB’s clinical application against ALI. Full article

Titanium alloys (Ti-6Al-4V) are widely used in the aerospace field. However, as a typical difficult-to-machine material, titanium alloys have a low thermal conductivity, a high chemical activity, and a significant adiabatic shear effect. In conventional milling (CM), the temperature in the cutting zone rises sharply, leading to tool adhesion, rapid wear, and damage to the workpiece surface. This article systematically investigated the influence of process parameters on the surface roughness, cutting force, and cutting temperature in the ultrasonic-vibration-assisted milling (UAM) process of titanium alloys, based on which multi-objective optimization process of the milling process parameters was conducted, by utilizing the grey relational analysis method. An orthogonal experiment with four factors and four levels was conducted. The effects of various process parameters on the surface roughness, cutting force, and cutting temperature were systematically analyzed for both UAM and CM. The grey relational analysis method was employed to transform the optimization problem of multiple process target parameters into a single-objective grey relational degree optimization problem. The optimized parameter combination was as follows: an ultrasonic amplitude of 6 μm, a spindle speed of 6000 rpm, a cutting depth of 0.20 mm, and a feed rate of 200 mm/min. The experimental results indicated that the surface roughness Sa was 0.268 μm, the cutting temperature was 255.39 °C, the cutting force in the X direction (FX) was 5.2 N, the cutting force in the Y direction (FY) was 7.9 N, and the cutting force in the Z direction (FZ) was 6.4 N. The optimization scheme significantly improved the machining quality and reduced both the cutting forces and the cutting temperature. Full article

The near-surface air temperature lapse rate (SATLR) is a crucial parameter in climate, hydrology, and ecology research conducted in mountainous regions. However, existing research has difficulty characterizing its dynamic changes on an hourly scale. Obtaining data with high spatiotemporal resolution in complex terrains using existing methods poses challenges. This study introduces a hierarchical method for estimating SATLR at high spatiotemporal resolutions based on Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) land surface temperature (LST) data and machine learning techniques. Based on reconstructed FY-4A AGRI LST data, this study downscales the 4 km resolution data to a 1 km resolution using machine learning. It then estimates the spatial distribution of near-surface air temperature (SAT) and normalized near-surface air temperature (nSAT) by integrating station observations. Subsequently, high spatiotemporal resolution SATLRs are estimated, and their spatial and temporal distribution characteristics in the Beijing–Tianjin–Hebei mountainous region are analyzed. The results indicate that the SATLR exhibits a predominant distribution of 2~6 °C/km annually across the study area. However, in specific regions such as Taihang Mountains in the southwest, Damajun Mountain in the northwest, and certain areas of central Beijing City, the SATLR exceeds 6 °C/km in depth. Conversely, in Chengde City in the northeast and Huapiling in Damajun Mountain in the northwest, the SATLR is shallower than 2 °C/km. Seasonally, the average SATLR displays significant variation, with 3~5 °C/km being prevalent in spring, summer, and autumn, and 2~4 °C/km in winter. Moreover, the diurnal SATLR patterns from the second to fifth altitude grades exhibit consistency throughout the year and across seasons, albeit with varying overall values at different altitudes. Notably, the SATLR of the first altitude grade demonstrates stability within a day at lower elevations. Full article

Land Surface Temperature (LST), as a core variable in the coupling of land–atmosphere energy transfers and ecological responses, relies heavily on the global coverage capacity of thermal infrared remote sensing (TIR-LST) for dynamic monitoring. Currently, the time reconstruction method of the TIR-LST products from China’s Fengyun polar-orbiting satellite under dynamic cloud interference remains under exploration. This study focuses on the Heihe River Basin in western China, and addresses the issue of cloud coverage in relation to the Fengyun-3C (FY-3C) satellite TIR-LST. An innovative spatiotemporal reconstruction framework based on multi-source data collaboration was developed. Using a hybrid ensemble learning framework of random forest and ridge regression, environmental parameters such as vegetation index (NDVI), land cover type (LC), digital elevation model (DEM), and terrain slope were integrated. A downscaling and multi-factor collaborative representation model for land surface temperature was constructed, thereby integrating the passive microwave LST and thermal infrared VIRR-LST from the FY-3C satellite. This produced a seamless LST dataset with 1 km resolution for the period of 2017–2019, with temporal continuity across space. The validation results show that the reconstructed data significantly improves accuracy compared to the original VIRR-LST and demonstrates notable spatiotemporal consistency with MODIS LST at the daily scale (annual R² ≥ 0.88, RMSE < 2.3 K). This method successfully reconstructed the FY-3C satellite’s 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking. Full article

Microwave sounding observations obtained from the National Oceanic and Atmospheric Administration (NOAA) and the European Meteorological Operational Satellite Program (METOP) satellites have been used for retrieving the cloud ice water path (IWP). However, the IWP algorithms developed in the past cannot be applied to the Fengyun-3F (FY-3F) microwave radiometers due to the differences in frequency of the primary channels and the fields of view. In this study, the IWP algorithm was tailored for the FY-3F satellite, and the retrieved IWP was compared with the fifth generation of reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5) and the Meteorological Operational Satellite-C (METOP-C) products. The results indicate that the IWP distribution retrieved from FY-3F observations demonstrates strong consistency with the cloud ice distributions in ERA5 data and METOP-C products in low-latitude regions. However, discrepancies are observed among the three datasets in mid- to high-latitude regions. ERA5 data underestimate the frequency of high IWP values and overestimate the frequency of low IWP values. The IWP retrieval results from satellite datasets demonstrate a high level of consistency. Furthermore, an analysis of the IWP time series reveals that the retrieval algorithm used in this study better captures variability and seasonal characteristics of IWP compared to ERA5 data. Additionally, a comparison of FY-3F retrieval results with METOP-C products shows a high correlation and generally consistent distribution characteristics across latitude bands. These findings confirm the high accuracy of IWP retrieval from FY-3F data, which holds significant value for advancing IWP research in China. Full article