Search Results (8,335)

This study proposes an improved YOLO11n-seg instance segmentation model to address the limitations of existing models in accurately identifying mature blueberries in complex greenhouse environments. Current methods often lack sufficient accuracy when dealing with complex scenarios, such as fruit occlusion, lighting variations, and target overlap. To overcome these challenges, we developed a novel approach that integrates a Spatial–Channel Adaptive (SCA) attention mechanism and a Dual Attention Balancing (DAB) module. The SCA mechanism dynamically adjusts the receptive field through deformable convolutions and fuses multi-scale color features. This enhances the model’s ability to recognize occluded targets and improves its adaptability to variations in lighting. The DAB module combines channel–spatial attention and structural reparameterization techniques. This optimizes the YOLO11n structure and effectively suppresses background interference. Consequently, the model’s accuracy in recognizing fruit contours improves. Additionally, we introduce Normalized Wasserstein Distance (NWD) to replace the traditional intersection over union (IoU) metric and address bias issues that arise in dense small object matching. Experimental results demonstrate that the improved model significantly improves target detection accuracy, recall rate, and mAP@0.5, achieving increases of 1.8%, 1.5%, and 0.5%, respectively, over the baseline model. On our self-built greenhouse blueberry dataset, the mask segmentation accuracy, recall rate, and mAP@0.5 increased by 0.8%, 1.2%, and 0.1%, respectively. In tests across six complex scenarios, the improved model demonstrated greater robustness than mainstream models such as YOLOv8n-seg, YOLOv8n-seg-p6, and YOLOv9c-seg, especially in scenes with dense occlusions. The improvement in mAP@0.5 and F1 scores validates the effectiveness of combining attention mechanisms and multiple metric optimizations, for instance, segmentation tasks in complex agricultural scenes. Full article

Fructose-1,6-bisphosphatase (FBP) is a crucial regulatory enzyme in sucrose synthesis and photosynthetic carbon assimilation, functioning through two distinct isoforms: cytosolic FBP (cyFBP) and chloroplastic FBP (cpFBP). However, the identification and functional characterization of FBP genes in Saccharum remains limited. In this study, we conducted a systematic identification and comparative genomics analyses of FBPs in three Saccharum species. We further examined their expression patterns across leaf developmental zones, spatiotemporal profiles, and responses to diurnal rhythms and hormonal treatments. Our analysis identified 95 FBP genes, including 44 cyFBPs and 51 cpFBPs. Comparative analyses revealed significant divergence in physicochemical properties, gene structures, and motif compositions between the two isoforms. Expression profiling indicated that both cyFBPs and cpFBPs were predominantly expressed in leaves, particularly in maturing and mature zones. During diurnal cycles, their expression peaked around the night–day transition, with cpFBPs exhibiting earlier peaks than cyFBPs. FBP genes in Saccharum spontaneum displayed greater diurnal sensitivity than those in Saccharum officinarum. Hormonal treatments further revealed significant regulatory divergence in FBP genes, both between isoforms and across species. Notably, cyFBP_2 and cpFBP_2 members consistently exhibited higher expression levels across all datasets, suggesting their pivotal roles in sugarcane physiology. These findings not only identify potential target genes for enhancing sucrose accumulation, but also highlight the breeding value of S. spontaneum and S. officinarum in sugarcane breeding. Full article

Pulmonary hypertension (PH) is a serious pulmonary vascular disease. Vascular remodeling, metabolic reprogramming, inflammation, and fibrosis are all major pathogenic mechanisms in PH. MicroRNAs (miRNAs) are small RNAs, about 20–24 nucleotides long, that play important regulatory roles in biological processes, and in recent years, miRNAs have been found to potentially play a regulatory role in the pathogenesis of PH, and also serve as biomarkers and therapeutic agents for PH. However, there is still a long way to go from these experimental findings to their implementation in clinical practice. This study reviews the potential role of miRNAs in the pathogenesis of PH and suggests future applications of miRNAs in PH. Full article

To study and quantify the impact of water storage on lake slope stability after the closure of an open-pit mine, we targeted slope control measures by large-scale parallel computing methods and strength reduction theory. This was based on a three-dimensional refined numerical model to simulate the evolution of slope stability under different water storage levels and backfilling management conditions, and to quantitatively assess the risk of slope instability through the spatial distribution of stability coefficients. This study shows that during the impoundment process, the slope stability has a nonlinear decreasing trend due to the decrease in effective stress caused by the increase in pore water pressure. When the water storage was at 0 m, the instability range is the largest, and the surface range is nearly 200 m from the edge of the pit; when the water level continued to rise to 50 m, the hydrostatic pressure of the pit lake water on the slope support effect began to appear, and the stability was improved, but there is still a wide range of unstable areas at the bottom. In view of the unstable area of the steep slope with soft rock in the north slope during the process of water storage, the management scheme of backfilling the whole bottom to −150 m was proposed, and the slope protection and pressure footing were formed by discharging the soil to −40 m in steps to improve the anti-slip ability of the slope. Full article

Porcine circovirus type 2 (PCV2) is a globally prevalent swine pathogen that induces immunosuppression, predisposing pigs to subclinical infections. In intensive farming systems, PCV2 persistently impairs growth performance and vaccine efficacy, leading to substantial economic losses in the swine industry. Emerging evidence suggests that certain viruses exploit Suppressor of Cytokine Signaling 3 (SOCS3), a key immune checkpoint protein, to subvert host innate immunity by suppressing cytokine signaling. While SOCS3 has been implicated in various viral infections, its regulatory role in PCV2 replication remains undefined. This study aims to elucidate the mechanisms underlying the interplay between SOCS3 and PCV2 during viral pathogenesis. Porcine SOCS3 was amplified using RT-PCR and stably overexpressed in PK-15 cells through lentiviral delivery. Bioinformatics analysis facilitated the design of three siRNA candidates targeting SOCS3. We systematically investigated the effects of SOCS3 overexpression and knockdown on PCV2 replication kinetics and host antiviral responses by quantifying the viral DNA load and the mRNA levels of cytokines. PCV2 infection upregulated SOCS3 expression at both transcriptional and translational levels in PK-15 cells. Functional studies revealed that SOCS3 overexpression markedly enhanced viral replication, whereas its knockdown suppressed viral proliferation. Intriguingly, SOCS3-mediated immune modulation exhibited a divergent regulation of antiviral cytokines: PCV2-infected SOCS3-overexpressing cells showed elevated IFN-β but suppressed TNF-α expressions, whereas SOCS3 silencing conversely downregulated IFN-β while amplifying TNF-α responses. This study unveils a dual role of SOCS3 during subclinical porcine circovirus type 2 (PCV2) infection: it functions as a host-derived pro-viral factor that facilitates viral replication while simultaneously reshaping the cytokine milieu to suppress overt inflammatory responses. These findings provide novel insights into the mechanisms underlying PCV2 immune evasion and persistence and establish a theoretical framework for the development of host-targeted control strategies. Although our results identify SOCS3 as a key host determinant of PCV2 persistence, the precise molecular pathways involved require rigorous experimental validation. Full article

The genus Panax contains traditional herbs that have been widely used in traditional medicine. The active constituents, collectively known as ginsenosides, are well characterized in the most representative species, P. notoginseng. However, the major bioactive chemical constituents of P. stipuleanatus together with P. vietnamensis are relatively less studied. In this study, an untargeted metabolomic analysis was performed in P. notoginseng, P. stipuleanatus, and P. vietnamensis using root and leaf organs. Further metabolomic differences in P. stipuleanatus were compared with those of the two most prevalent species. The analysis results revealed tissue-specific qualitative and quantitative metabolic differences in each species. Several differentially accumulated metabolites were enriched in the biosynthesis of secondary metabolites, including the biosynthesis of ginsenosides I. The ginsenosides Rb1, Rf, Rg1, Rh1, Rh8, and notoginsenosides E, M, and N had a higher abundance level in the roots of both P. notoginseng and P. vietnamensis. In P. stipuleanatus, the accumulation of potentially important ginsenosides is mainly found in the leaf. In particular, the dammarane-type ginsenosides Rb3, Rb1, Mx, and F2 as well as the notoginsenosides A, Fe, Fa, Fd, L, and N were identified to have a higher accumulation in the leaf. The strong positive correlation network of different ginsenosides probably enhanced secondary metabolism in each species. The comparative analysis revealed a significant differential accumulation of metabolites in the leaves of both species. The various compounds of dammarane-type ginsenoside, such as Rb1, Rg1, Rg6, Rh8, Rh10, Rh14, and majoroside F2, had a significantly higher concentration level in the leaves of P. stipuleanatus. In addition, several notoginsenoside compounds such as A, R1, Fe, Fd, and Ft1 showed a higher abundance in the leaf. These results show that the abundance level of major ginsenosides is significant in P. stipuleanatus and provides an important platform to improve the ginsenoside quality of Panax species. Full article

The southeastern Yunnan region in the southwestern Nanpanjiang Basin is one of the most important manganese enrichment zones in China. Manganese mineralization is mainly confined to marine mud–sand–carbonate interbeds of the Middle Triassic Ladinian Falang Formation (T₂f), which contains several medium to large deposits such as Dounan, Baixian, and Yanzijiao. However, the geological processes that control manganese mineralization in this region remain insufficiently understood. Understanding the tectonic evolution of the basin is therefore essential to unravel the mechanisms of Middle Triassic metallogenesis. This study investigates how rift-related tectonic activity influences manganese ore formation. This study integrates global gravity and magnetic field models (WGM2012, EMAG2v3), audio-frequency magnetotelluric (AMT) profiles, and regional geological data to investigate ore-controlling structures. A distinct gravity low–magnetic high belt is delineated along the basin axis, indicating lithospheric thinning and enhanced mantle-derived heat flow. Structural interpretation reveals a rift system with a checkerboard pattern formed by intersecting NE-trending major faults and NW-trending secondary faults. Four hydrothermal plume centers are identified at these fault intersections. AMT profiles show that manganese ore bodies correspond to stable low-resistivity zones, suggesting fluid-rich, hydrothermally altered horizons. These findings demonstrate a strong spatial coupling between hydrothermal activity and mineralization. This study provides the first identification of the internal rift architecture within the Nanpanjiang Basin. The basin-scale rift–graben system exerts first-order control on sedimentation and manganese metallogenesis, supporting a trinity model of tectonic control, hydrothermal fluid transport, and sedimentary enrichment. These insights not only improve our understanding of rift-related manganese formation in southeastern Yunnan but also offer a methodological framework applicable to similar rift basins worldwide. Full article

This study utilized eye-tracking technology to explore the role of visual word segmentation cues in Tibetan reading, with a particular focus on the effects of dictionary-based and psychological word segmentation on reading and lexical recognition. The experiment employed a 2 × 3 design, comparing six conditions: normal sentences, dictionary word segmentation (spaces), psychological word segmentation (spaces), normal sentences (green), dictionary word segmentation (color alternation), and psychological word segmentation (color alternation). The results revealed that word segmentation with spaces (whether dictionary-based or psychological) significantly improved reading efficiency and lexical recognition, whereas color alternation showed no substantial facilitative effect. Psychological and dictionary word segmentation performed similarly across most metrics, though psychological segmentation slightly outperformed in specific indicators (e.g., sentence reading time and number of fixations), and dictionary word segmentation slightly outperformed in other indicators (e.g., average saccade amplitude and number of regressions). The study further suggests that Tibetan reading may involve cognitive processes at different levels, and the basic units of different levels of cognitive processes may not be consistent. These findings hold significant implications for understanding the cognitive processes involved in Tibetan reading and for optimizing the presentation of Tibetan text. Full article

Slope stability assessment is a critical component of engineering safety. Conventional analytical methods frequently struggle to integrate heterogeneous slope data and model intricate failure mechanisms, thereby constraining their efficacy in practical engineering scenarios. To tackle these issues, this study presents a novel slope stability classification model grounded in the Optuna-TPE-CatBoost framework. By leveraging the Tree-structured Parzen Estimator (TPE) within the Optuna framework, the model adaptively optimizes CatBoost hyperparameters, thus enhancing prediction accuracy and robustness. It incorporates six key features—slope height, slope angle, unit weight, cohesion, internal friction angle, and the pore pressure ratio—to establish a comprehensive and intelligent assessment system. Utilizing a dataset of 272 slope cases, the model was trained with k-fold cross-validation and dynamic class imbalance strategies to ensure its generalizability. The optimized model achieved impressive performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.926, an accuracy of 0.901, a recall of 0.874, and an F1-score of 0.881, outperforming benchmark algorithms such as XGBoost, LightGBM, and the unoptimized CatBoost. Validation via engineering case studies confirms that the model accurately evaluates slope stability across diverse scenarios and effectively captures the complex interactions between key parameters. This model offers a reliable and interpretable solution for slope stability assessment under complex failure mechanisms. Full article

This paper presents a comparative analysis of electric field (E-field) mitigation in inductive power transfer (IPT) systems. It focuses on how distributed capacitor placement interacts with coil topology to influence E-field emissions. The study compares traditional sequential-winding coils and the alternating voltage phase coil (AVPC), which employs a sequential inversion winding (SIW) structure to enforce a 180° phase voltage opposition between adjacent turns. While capacitor segmentation is a known method for E-field reduction, this work is the first to systematically evaluate its effects across both conventional and phase-inverted coils. The findings reveal that capacitor placement serves as a topology-dependent design parameter. Finite Element Method (FEM) simulations and experimental validation show that while capacitor placement has a moderate influence on traditional coils due to in-phase voltage relationships, AVPC coils are highly sensitive to segmentation patterns. When capacitors align with the SIW phase structure, destructive interference significantly reduces E-field emissions. Improper capacitor placement disrupts phase cancellation and negates this benefit. This study resolves a critical design gap by establishing that distributed compensation acts as a tuning mechanism in conventional coils but becomes a primary constraint in phase-inverted topologies. The results demonstrate that precise capacitor placement aligned with the coil topology significantly enhances E-field mitigation up to 60% in AVPC coils, greatly outperforming traditional coil configurations and providing actionable guidance for high-power wireless charging applications. Full article

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments. Full article

This study proposes a method for estimating the kinetic coefficient of friction (COF) for asphalt pavements by improving and applying Persson’s friction theory. The method utilizes the power spectral density (PSD) of the top surface topography instead of the full PSD to better reflect the actual contact conditions. This approach avoids including deeper roughness components that do not contribute to real rubber–pavement contact due to surface skewness. The key aspect of the method is determining an appropriate cutting plane to isolate the top surface. Four cutting strategies were evaluated. Results show that the cutting plane defined at 0.5 times the root mean square (RMS) height exhibits the highest robustness across all pavement types, with the estimated COF closely matching the measured values for all four tested surfaces. This study presents an improved method for estimating the kinetic coefficient of friction (COF) of asphalt pavements by employing the power spectral density (PSD) of the top surface roughness, rather than the total surface profile. This refinement is based on Persson’s friction theory and aims to exclude the influence of deep surface irregularities that do not make actual contact with the rubber interface. The core of the method lies in defining an appropriate cutting plane to isolate the topographical features that contribute most to frictional interactions. Four cutting strategies were investigated. Among them, the cutting plane positioned at 0.5 times the root mean square (RMS) height demonstrated the best overall applicability. COF estimates derived from this method showed strong consistency with experimentally measured values across all four tested asphalt pavement surfaces, indicating its robustness and practical potential. Full article

With the rapid increase in global lithium demand, the exploration of newly discovered lithium in the bauxite of the Wenshan area in southeastern Yunnan has become increasingly important. However, the current research on clay-type lithium in the Wenshan area has primarily focused on local exploration, and large-scale predictive metallogenic studies remain limited. To address this, this study utilized multi-source remote sensing data from ZY1-02D and ASTER, combined with ALOS 12.5 m DEM and Sentinel-2 imagery, to carry out remote sensing mineral identification, structural interpretation, and prospectivity mapping for clay-type lithium in the Wenshan area. This study indicates that clay-type lithium in the Wenshan area is controlled by NW, EW, and NE linear structures and are mainly distributed in the region from north of the Wenshan–Malipo fault to south of the Guangnan–Funing fault. High-value areas of iron-rich silicates and iron–magnesium minerals revealed by ASTER data indicate lithium enrichment, while montmorillonite and cookeite identification by ZY1-02D have strong indicative significance for lithium. Field verification samples show the highest Li₂O content reaching 11,150 μg/g, with six samples meeting the comprehensive utilization criteria for lithium in bauxite (Li₂O ≥ 500 μg/g) and also showing an enrichment of rare earth elements (REEs) and gallium (Ga). By integrating stratigraphic, structural, mineral identification, geochemical characteristics, and field verification data, ten mineral exploration target areas were delineated. This study validates the effectiveness of remote sensing technology in the exploration of clay-type lithium and provides an applicable workflow for similar environments worldwide. Full article

Proanthocyanidins are a subclass of flavonoids formed through a poorly understood polymerization process that forms chains of 3–30 catechins and epi-catechins. Proanthocyanidins serve as UV protectants and antifeedants that accumulate in diverse plant species, including the lotus. To identify candidate genes underlying proanthocyanidin synthesis and polymerization, we generated and functionally annotated transcriptomes from seedpods and seed epicarps of two lotus cultivars, “Guoqing Hong” and “Space Lotus”, which accumulate markedly divergent proanthocyanidin levels across the immature, near-mature, and mature developmental stages. Our transcriptome analysis was based on a total of 262.29 GB of raw data. We aligned the transcriptome data with the lotus genome and obtained an alignment efficiency that ranged from 91.74% to 96.44%. Based on the alignment results, we discovered 4774 new genes and functionally annotated 3232 genes. A total of 14,994 differentially expressed genes (DEGs) were identified from two-by-two comparisons of transcript libraries. We found 61 DEGs in the same developmental stage in the same tissue of different species. Comparative transcriptome analysis of seedpods and seed epicarps from two cultivars identified 14,994 differentially expressed genes (DEGs), of which 10 were functionally associated with proanthocyanidin synthesis and 9 were possibly implicated in the polymerization reactions. We independently quantified the expression of the candidate genes using qRT-PCR. Significant differences in the expression of candidate genes in different tissues and periods of lotus species are consistent with particular genes contributing to the polymerization of catechins and epi-catechins into proanthocyanidins in lotus seedpods and seed epicarps. Full article