Search Results (479)

Coral reefs are facing a catastrophic decline due to climate-induced bleaching, threatening critical marine biodiversity. Automated, large-scale monitoring is essential; however, modern object detectors are hindered by two fundamental limitations in complex underwater scenes: a spatial reasoning deficit in their decoupled heads, which inhibits robust multi-scale feature integration, and a feature robustness deficit, which renders deterministic networks vulnerable to stochastic visual variations. To address these limitations, we propose Coral-YOLO, a novel framework for detection and forecasting. We introduce the Holistic Attention Block Head (HAB-Head), which enables deep cross-scale reasoning through explicit feature interaction, and MCAttention, a randomized training mechanism that enables the network to learn scale-invariant features with inherent robustness. Evaluated on our newly curated, multi-year CR-Mix dataset, Coral-YOLO achieves a state-of-the-art 50.3% AP (average precision at IoU threshold 0.5:0.95, following COCO metrics), representing a +1.8 percentage point improvement over the YOLOv12-m baseline, with particularly pronounced gains on small objects (+2.6 percentage points in AP_S). Crucially, its integrated temporal forecasting module achieves 82.7% accuracy in predicting future coral health, substantially outperforming conventional methods. Coral-YOLO sets a new performance benchmark and enables proactive reef conservation. It provides a powerful tool to identify at-risk corals long before severe bleaching becomes visually apparent. Full article

To enhance the operational efficiency of energy storage converters in grid-connected systems with high renewable penetration, this study systematically investigates key technologies of active neutral-point clamped (ANPC) topology under “electrical–thermal–mechanical” multi-physical field coupling. The study reviews recent progress in structural design, modulation strategies, and fault-tolerant control, highlighting their impact on efficiency, reliability, and power density. At the structural stage, a hybrid SiC/IGBT device configuration combined with a three-dimensional stacked bus reduces conduction loss and achieves parasitic inductance. In the modulation stage, improved finite-set model predictive control and adaptive space vector modulation shorten computation time to 20 µs and keep total harmonic distortion (THD) within 2.8%. System-level evaluations demonstrate that a 250 kW ANPC converter attains a peak efficiency of 99.1%, a power density of 4.5 kW/kg, and a mean time between failure exceeding 150,000 h. These findings reveal a clear transition from single-objective performance improvement toward integrated multi-physics co-design. By unifying advanced modulation, intelligent fault-tolerant control, and multi-field coupling optimization, ANPC-based converters advance converters to a new stage of higher efficiency, reliability, and stability. The results provide essential technical support for next-generation power conversion systems in renewable-rich grids. Full article

Nickel-based GH3536 alloys prepared by laser powder bed fusion (LPBF) exhibit a mismatch between strength and ductility during the tensile process, which severely restricts their engineering applications in the aerospace field. In order to optimize their performance, this study adopted hot isostatic pressing (HIP) and subsequent heat treatment (HT) to modify the material. The microstructural evolution of the HIP+HT-treated GH3536 alloy during deformation, including grain rotation, grain boundary migration, and dislocation slip transfer behaviors, was systematically investigated at room temperature using in situ tensile experiments. The relationship between the microstructure and mechanical properties was elucidated in greater depth by combining theoretical calculations. The experimental results show that after HIP+HT treatment, the elongation of the alloy increased significantly from 36.5% in the as-built LPBF condition to 45.3 ± 1.6% without a significant reduction in ultimate tensile strength. The plasticity enhancement is mainly attributed to the elimination of defects and the formation of annealing twins. In addition, the formation of substructures inside the grains also delays the fracture of the specimen to some extent. This study is expected to provide a reference for the subsequent optimization of the mechanical properties of alloys via heat treatment processes. Full article

The increasing burial depth of deep shale formations in the southern Sichuan leads to more complex in situ stresses and geological structures, which in turn raises the challenges of hydraulic fracturing. Although enlarging the treatment scale and injection rate can enhance reservoir stimulation, the intensive development of faults and fractures in deep shale formations aggravates stress instability, inducing casing deformation, fracture communication, and other engineering risks that constrain efficient shale gas production. In this study, a cross-scale geomechanical model linking the regional to near-wellbore domains was constructed. A dynamic evolution equation was established based on flow–stress coupling, and a numerical conversion from the geological model to the finite element model was implemented through self-programming, thereby developing a simulation method for dynamic geomechanical evolution during hydraulic fracturing. Results indicate that dynamic variations in pore pressure dominate stress redistribution, while near-wellbore heterogeneity and mechanical property distribution significantly affect prediction accuracy. The injection of fracturing fluid generates a high-pressure gradient that drives pore pressure diffusion along fracture networks and faults, exhibiting a strong near-wellbore but weak far-field non-steady spatial attenuation. As the pore pressure increases, the peak value reaches 1.4 times the original pressure. The triaxial stress shows a negative correlation and decreases. The horizontal minimum principal stress shows the most significant drop, with a reduction of 15.79% to 20.68%, while the vertical stress changes the least, with a reduction of 5.7% to 7.14%. Compared with the initial stress state, the horizontal stress difference increases during fracturing. Rapid pore-pressure surges and fault distributions further trigger stress reorientation, with the magnitude of rotation positively correlated with the intensity of pore-pressure variation. The high porosity and permeability characteristics of the initial fracture network lead to a rapid attenuation of the stress around the wellbore. In the middle and later stages, as the pressure balance is achieved through fracture filling, the pore pressure rises and the stress decline tends to stabilize. The findings provide significant insights into the dynamic stress evolution of deep shale reservoirs during fracturing and offer theoretical support for optimizing fracturing design and mitigating engineering risks. Full article

To address the issues of insufficient protection and poor durability in concrete during service, this study developed a novel polymer–silicate composite gel system by combining silane with fluorocarbon resin emulsion and applied it to mortar specimens. The chloride ion resistance enhancement of mortar provided by the novel gel system was evaluated using the RCM method and natural chloride ion penetration tests, with SEM images employed to analyze its anti-permeation mechanism. Results indicate that the chloride ion migration coefficient of the novel composite gel system is 4.91 × 10⁻¹² m²/s, representing a 63.97% reduction compared to the single fluorocarbon gel system. Within the 0–5 mm depth range, free chloride ion contents at 14, 28, and 56 days decreased by 55.35%, 50.10%, and 43.64%, respectively, demonstrating excellent resistance to chloride penetration. Acid and alkali resistance tests demonstrated that the system retained the inherent corrosion resistance of the fluorocarbon component. Carbonation tests demonstrated that the system exhibited a slight decrease in carbonation resistance compared with the pure fluorocarbon gel system, while still maintaining a satisfactory performance level. Overall, the polymer-silicate composite gel system significantly enhanced the mortar’s resistance to chloride ion penetration. Full article

Rapid, non-destructive assessment of biomass composition is essential for advancing Miscanthus spp. breeding and bioenergy production. This study aimed to develop and validate high-throughput near-infrared spectroscopy (NIRS) models for key chemical components in Miscanthus biomass. A robust calibration set was constructed from 107 diverse samples by combining two key species, Miscanthus sacchariflorus and M. lutarioriparius, to enhance chemical variability and create broadly applicable models. Partial Least Squares (PLS) regression models were developed using this dataset, comparing full-spectrum performance against models optimized with three feature selection algorithms: CARS, VCPA-GA, and VCPA-IRIV. All feature selection methods significantly enhanced predictive accuracy. Notably, the CARS-PLS models yielded excellent performance for cellulose (R²v = 0.98; RPD = 7.38), hemicellulose (R²v = 0.95, RPD = 4.35), lignin (R²v = 0.96, RPD = 5.40), and moisture (R²v = 0.98, RPD = 7.18), while the VCPA-IRIV-PLS model was superior for ash content (R²v = 0.96, RPD = 5.13). Overall, NIRS coupled with advanced feature selection provides a powerful, rapid protocol for Miscanthus biomass analysis, poised to accelerate germplasm evaluation and industrial quality control in the bioenergy sector. Full article

Ice krill (Euphausia crystallorophias) play a key role in the Antarctic coastal ecosystem, yet its spatial growth variability remains poorly understood. This study examined 5298 krill individuals from 52 stations across the Amundsen Sea, transitional waters, and the Ross Sea, collected between 2020 and 2024. Length–weight relationships (LWR) were constructed to derive the condition factor a and the allometric growth exponent b, followed by regional comparisons and environmental response analyses using boxplots, redundancy analysis (RDA), and generalized additive models (GAM). Boxplots revealed that a was significantly higher in the Amundsen Sea and transitional zone than in the Ross Sea, while b was highest and most variable in the Amundsen Sea. RDA indicated that a was primarily associated with depth, latitude, mean temperature, and mean salinity, whereas b was influenced by sea surface temperature, chlorophyll-a, sea ice concentration, and longitude. GAM further showed nonlinear responses of a to mean temperature, mean salinity, and depth, with peaks near −0.5 °C, 34.2 PSU, and 3500 m, respectively. These results suggest that krill in deep, cold, and less-productive transitional zone allocate more energy to body condition (high value a), while those in warmer, moderately productive regions like the Amundsen Sea invest more in structural growth (high value b). This study provides new insights into the environmentally driven growth strategies of ice krill and contributes to understanding its ecological adaptability under changing climatic and oceanographic conditions. Full article

Leaf area index (LAI) is a critical indicator of canopy architecture and physiological performance, serving as a key parameter for crop growth monitoring and management. Although UAV multispectral imagery provides rich spectral and spatial information, the limitations of single texture features for LAI estimation still require further exploration. To address this issue, this study developed a multi-source feature fusion framework that integrates vegetation indices (VIs), texture features (TFs), and texture indices (TIs) within a stacked ensemble approach combining Partial Least Squares Regression (PLSR) with Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) algorithms to estimate maize LAI.A field experiment was conducted under three planting densities (42,000, 63,000, and 84,000 plants ha⁻¹) and four nitrogen rates (0, 80, 160, 240 kg N ha⁻¹) to assess the potential of UAV-based multispectral imagery for maize LAI estimation. The results show that when using partial least squares regression (PLSR) combined with RF, SVM and GBDT to estimate maize LAI, the R² values are 0.653, 0.697 and 0.634, and the RMSE is 0.650, 0.608 and 0.668, respectively, when only vegetation indices (VIs) is used as input. After texture features (TFs) incorporation, the R² increases to 0.717, 0.794, and 0.801, and the RMSE decreases to 0.587, 0.500, and 0.492. Further inclusion of the texture indices (TIs) raises the R² to 0.789, 0.804, and 0.844, with RMSE of 0.506, 0.489, and 0.436, respectively. Independent test set validation under contrasting conditions confirmed that our multi-model fusion framework (PLSR+GBDT) with multi-source feature fusion (VIs+TFs+TIs) effectively estimated LAI, achieving an R² of 0.859 and 0.794. These results demonstrate that multi-source feature integration via machine learning enables robust and accurate estimation of maize LAI, providing a valuable tool for precision agriculture and crop growth monitoring. Full article

Traditional methods for inspecting large-span roofs face challenges such as extensive measurement areas, difficult positioning, low efficiency, and insufficient utilization of secondary analysis of inspection data. To address these issues, this study proposes a secondary analysis method based on UAV technology using oblique photography and point cloud data, aimed at rapidly detecting roof surface slope variations and predicting drainage pathways. The research employs a gimbal-equipped UAV to capture tilted images, generates point cloud data through aerial triangulation, and subsequently achieves 3D modeling and texture mapping. Based on the diagonal intersection grid of the roof surface, a point cloud feature processing algorithm is used to extract the axis, and the layout and slope of each region are calculated and compared with design drawings. The results demonstrate that this method can efficiently and accurately detect the arrangement of the roof surface grid and slope variations, providing significant guidance for the subsequent installation and drainage design of metal roofs. Full article

Background/Objectives: Triple-negative breast cancer (TNBC) is among the most aggressive subtypes, lacking estrogen, progesterone, and HER2 receptors, which limits the efficacy of targeted therapies. Standard treatments often fail due to rapid drug resistance and poor long-term outcomes. Repurposing approved drugs with anticancer potential offers a promising alternative. Disulfiram (DSF), an FDA-approved alcohol-aversion drug, forms a copper complex [Cu(DDC)₂] with potent anticancer activity, but its clinical translation is hindered by poor solubility, limited stability, and inefficient delivery. Methods: Here, we present an amphiphilic dendrimer-stabilized [Cu(DDC)₂] nanoparticle (NP) platform synthesized via the stabilized metal ion ligand complex (SMILE) method. Results: The optimized nanocarrier achieved high encapsulation efficiency, enhanced serum stability, and potent cytotoxicity against TNBC cells. It induced immunogenic cell death (ICD) characterized by calreticulin exposure and ATP release, while modulating the tumor microenvironment by downregulating MMP-3, MMP-9, VEGF, and vimentin, and restoring epithelial markers. In a 4T1 TNBC mouse model, systemic [Cu(DDC)₂] NP treatment significantly inhibited tumor growth without combinational chemo- or radiotherapy. Conclusions: This DSF-based metal–organic NP integrates drug repurposing, immune activation, and tumor microenvironment remodeling into a single platform, offering strong translational potential for treating aggressive breast cancers. Full article

Antarctic krill (Euphausia superba) is a keystone species in the marine ecosystem of the Antarctic Ocean, bringing about significant ecological and economic value. The spatio-temporal distribution of Antarctic krill directly affects commercial fishing; meanwhile, changes in krill population structure play a crucial role in maintaining the balance of the Southern Ocean ecosystem. This study analyzed six years of midwater trawl data, including over 160,000 krill length measurements, to elucidate spatio-temporal dynamics and population composition, providing actionable insights for improved fishery management. Here, we reveal southward migration shifts in krill fishing grounds, with smaller individuals favoring ice-rich southern latitudes. Commercial krill fishing operations preferentially targeted high-density fishing grounds rather than selecting larger individuals. Among the catches, the age 1+ class accounted for the highest proportion at 42.80%, followed by the age 2+ class at 39.42%, with individuals ≥3+ accounting for 17.44%. Although the mean krill length experienced a decline in 2017, it demonstrated a sustained recovery in subsequent years, reaching peak dimensions in 2022. This maximum-growth year also exhibited the highest proportion (12.6%) of individuals within ≥4 age classes. Consequently, the sustained increase in fishing effort in recent years has not resulted in a reduction in the size of individual krill. The mean krill length showed a significant positive correlation with the depth (r = 0.36, p < 0.01) and temperature (r = 0.26, p < 0.01) of the krill cluster, and a significant negative correlation with resource density (r = −0.20, p < 0.01), year (ρ = −0.31, p < 0.01) and latitude (ρ = −0.31, p < 0.01). The length exhibited U-shaped temporal trends, and latitudinal and longitudinal nonlinearity. Body size was positively correlated with depth (p < 0.01), whereas as temperature increased, body size first increased and then remained constant. As density increased, the mean krill length increased first and then slowly decreased. Recent warming intensifies population shifts, with potential cascading effects on ecosystem structure and carbon sequestration. Full article

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations. Full article

Cold-bonded (CB) fly ash aggregate, an eco-friendly material derived from industrial by-products, is used to fully replace natural coarse aggregate in producing lightweight concrete (LWC-CB). This study systematically investigates the post-high-temperature mechanical properties and damage mechanisms of LWC-CB. Specimens exposed to ambient temperature (10 °C) and elevated temperatures (200 °C, 400 °C, 600 °C) underwent cubic compression tests, with surface deformation monitored via digital image correlation (DIC). Experimental results indicate that the strength retention of LWC-CB is approximately 6% superior to ordinary concrete below 500 °C, beyond which its performance converges. Damage analysis reveals a transition in failure mode: at ambient temperature, shear failure is governed by the low intrinsic strength of CB aggregates, while after high-temperature exposure, damage localizes within the mortar and the interfacial transition zone (ITZ) due to mortar micro-cracking and thermal mismatch. To elucidate these mechanisms, a three-dimensional mesoscale model was developed and validated, effectively characterizing the internal multiphase structure at room temperature. Furthermore, a homogenization model was established to analyze the macroscopic thermo-mechanical response. The numerical simulations show strong agreement with experimental data, with a maximum deviation of 15% at 10 °C and 3% after high-temperature exposure, confirming the model’s accuracy in capturing the performance evolution of LWC-CB. Full article

Hyperspectral remote sensing provides a powerful tool for crop nutrient monitoring and precision fertilization, yet its application is hindered by high-dimensional redundancy and inter-band collinearity. This study aimed to improve maize nitrogen estimation by constructing three types of two-dimensional full-band spectral indices—Difference Index (DI), Simple Ratio Index (SRI), and Normalized Difference Index (NDI)—combined with spectral preprocessing methods (raw spectra (RAW), first-order derivative (FD), and second-order derivative (SD)). To optimize feature selection, three strategies were evaluated: Grey Relational Analysis (GRA), Pearson Correlation Coefficient (PCC), and Variable Importance in Projection (VIP). These indices were then integrated into machine learning models, including Backpropagation Neural Network (BP), Random Forest (RF), and Support Vector Regression (SVR). Results revealed that spectral index optimization substantially enhanced model performance. NDI consistently demonstrated robustness, achieving the highest grey relational degree (0.9077) under second-derivative preprocessing and improving BP model predictions. PCC-selected features showed superior adaptability in the RF model, yielding the highest test accuracy under raw spectral input (R² = 0.769, RMSE = 0.0018). VIP proved most effective for SVR, with the optimal SD–VIP–SVR combination attaining the best predictive performance (test R² = 0.7593, RMSE = 0.0024). Compared with full-spectrum input, spectral index optimization effectively reduced collinearity and overfitting, improving both reliability and generalization. Spectral index optimization significantly improved inversion accuracy. Among the tested pipelines, RAW-PCC-RF demonstrated robust stability across datasets, while SD-VIP-SVR achieved the highest overall validation accuracy (R² = 0.7593, RMSE = 0.0024). These results highlight the complementary roles of stability and accuracy in defining the optimal pipeline for maize nitrogen inversion. This study highlights the pivotal role of spectral index optimization in hyperspectral inversion of maize nitrogen content. The proposed framework provides a reliable methodological basis for non-destructive nitrogen monitoring, with broad implications for precision agriculture and sustainable nutrient management. Full article

The Baijianshan deposit is the sole skarn Zn-Cu polymetallic deposit in the Xiaoshitouquan ore field, Xinjiang, China. Its ore genesis remains controversial, which hinders understanding of the relationship between skarn-type Zn-Cu and adjacent epithermal Ag-Cu-Pb-Zn mineralization and consequently impedes further regional exploration. LA-ICP-MS U-Pb dating on zircons from the granite and granite porphyry from the mining area yielded ages of 311 ± 1.7 Ma and 312 ± 1.6 Ma, respectively. The corresponding zircon ε_Hf(t) values and T_DM ages are 8.7–9.9 and 624–555 Ma for the granite, and 7.2–9.9 and 673–552 Ma for the granite porphyry. These granites are metaluminous, high-K calc-alkaline I-type granites, with high LREE/HREE ratios (4.92–9.03) and pronounced negative Eu anomalies. They are enriched in K, Th, U, Zr, and Hf, with significant depletions in Sr, P, and Ti. Combined geological and geochemical evidence indicate that these Late Carboniferous granites were derived from the juvenile crustal and formed in subduction-related back basin. Two-phase aqueous inclusions in the ore-bearing quartz and calcite have homogenization temperatures ranging from 117 to 207 °C and 112 to 160 °C, respectively, with the salinities in the ranges of 0.18~7.17 and 0.53~5.26 wt% NaCl eq. The S and Pb isotopic compositions of sulfides in the ores indicate that the ore-forming metals were sourced from the medium-acidic magmatite. The δ¹⁸O_H2O and δD_H2O values of hydrothermal fluids range from −6.97% to −5.84% and −106.8% to −99.6%, respectively, suggesting that the ore-forming fluids originated from the mixing of magmatic and meteoric water. Fluid mixing and corresponding conductive cooling were identified as the principal mechanism triggering the metallic mineral precipitation. The Baijianshan skarn Zn-Cu polymetallic deposit shares contemporaneous magmatic-mineralization ages and analogous material sources with the epithermal polymetallic deposits in the Xiaoshitouquan ore field, collectively constituting a unified skarn-epithermal metallogenic system. This hypothesis indicates that the deep parts of the epithermal deposits within the Yamansu volcanic rocks possess potential for exploring the porphyry-skarn-type deposits. Full article