Search Results (164)

Developing ultra-high-temperature geothermal resources is challenging, as traditional drilling fluids, including foam systems, lack thermal stability above 160 °C. To address this key technical bottleneck, this study delves into the screening principles for high-temperature-resistant foaming agents and foam stabilizers. Through high-temperature aging experiments (foaming performance evaluated up to 240 °C and rheological/filtration properties evaluated after aging at 200 °C), specific additives were selected that still exhibit good foaming and foam-stabilizing performance under high-temperature and high-salinity conditions. Building on this, the foam drilling fluid system formulation was optimized using an orthogonal experimental design. The optimized formulations were systematically evaluated for their density, volume, rheological properties (apparent viscosity and plastic viscosity), and filtration properties (API fluid loss and HTHP fluid loss) before and after high-temperature aging (at 200 °C). The research results indicate that specific formulation systems exhibit excellent high-temperature stability and particularly outstanding performance in filtration control, with the selected foaming agent FP-1 maintaining good performance up to 240 °C and optimized formulations demonstrating excellent HTHP fluid loss control at 200 °C. This provides an important theoretical basis and technical support for further research and field application of foam drilling fluid systems for deep high-temperature geothermal energy development. Full article

The existing deep learning-based Airborne Laser Scanning (ALS) point cloud semantic segmentation methods require a large amount of labeled data for training, which is not always feasible in practice. Insufficient training data may lead to over-fitting. To address this issue, we propose a novel Multi-branch Feature Extractor (MFE) and a three-stage transfer learning strategy that conducts pre-training on multi-source ALS data and transfers the model to another dataset with few samples, thereby improving the model’s generalization ability and reducing the need for manual annotation. The proposed MFE is based on a novel multi-branch architecture integrating Neighborhood Embedding Block (NEB) and Point Transformer Block (PTB); it aims to extract heterogeneous features (e.g., geometric features, reflectance features, and internal structural features) by leveraging the parameters contained in ALS point clouds. To address model transfer, a three-stage strategy was developed: (1) A pre-training subtask was employed to pre-train the proposed MFE if the source domain consisted of multi-source ALS data, overcoming parameter differences. (2) A domain adaptation subtask was employed to align cross-domain feature distributions between source and target domains. (3) An incremental learning subtask was proposed for continuous learning of novel categories in the target domain, avoiding catastrophic forgetting. Experiments conducted on the source domain consisted of DALES and Dublin datasets and the target domain consists of ISPRS benchmark dataset. The experimental results show that the proposed method achieved the highest OA of 85.5% and an average F1 score of 74.0% using only 10% training samples, which means the proposed framework can reduce manual annotation by 90% while keeping competitive classification accuracy. Full article

Plant microbiomes are vital for the growth and health of their host. Tree-associated microbiomes are shaped by multiple factors, of which the host is one of the key determinants. Whether different host genotypes affect the structure and diversity of the tissue-associated microbiome and how specific taxa enriched in different tree tissues are not yet well illustrated. Chinese fir (Cunninghamia lanceolata) is an important tree species for both economy and ecosystem in the subtropical regions of Asia. In this study, we investigated the tissue-specific fungal community structure and diversity of nine different Chinese fir genotypes (39 years) grown in the same field. With non-metric multidimensional scaling (NMDS) analysis, we revealed the divergence of the fungal community from rhizosphere soil (RS), fine roots (FRs), and thick roots (TRs). Through analysis with α-diversity metrics (Chao1, Shannon, Pielou, ACE, Good‘s coverage, PD-tree, Simpson, Sob), we confirmed the significant difference of the fungal community in RS, FR, and TR samples. Yet, the overall fungal community difference was not observed among nine genotypes for the same tissues (RS, FR, TR). The most abundant fungal genera were Russula in RS, Scytinostroma in FR, and Subulicystidium in TR. Functional prediction with FUNGuild analysis suggested that ectomycorrhizal fungi were commonly enriched in rhizosphere soil, while saprotroph–parasite and potentially pathogenic fungi were more abundant in root samples. Specifically, genotype N104 holds less ectomycorrhizal and pathogenic fungi in all tissues (RS, FR, TR) compared to other genotypes. Additionally, significant correlations of several endophytic fungal taxa (Scytinostroma, Neonothopanus, Lachnum) with the growth traits (tree height, diameter, stand volume) were observed. This addresses that the interaction between tree roots and the fungal community is a reflection of tree growth, supporting the “trade-off” hypothesis between growth and defense in forest trees. In summary, we revealed tissue-specific, as well as host genotype-specific and genotype-common characters of the structure and functions of their fungal communities. Full article

To address the issues of burr formation, structural deformation, and tearing in the conventional machining of Nomex honeycomb composites, this study aims to clarify the mechanisms by which ultrasonic vibration-assisted cutting enhances machining quality. A multi-scale analysis framework is developed to examine the effects of ultrasonic vibration on fiber distribution, cell-level shear response, and the overall cutting mechanics. At the microscale, analyses show that ultrasonic vibration mitigates stress concentrations, thereby shortening fiber length. At the mesoscale, elastic buckling and plastic yielding models show that ultrasonic vibration lowers shear strength and modifies the deformation. A macro-scale comparison of cutting behavior with and without ultrasonic vibration was conducted. The results indicate that the intermittent contact effect induced by vibration significantly reduces cutting force. Specifically, at an amplitude of 40 μm, the cutting force decreased by approximately 29.7% compared to the condition without ultrasonic vibration, with an average prediction error below 8.6%. Compared to conventional machining, which causes the honeycomb angle to deform to approximately 130°, ultrasonic vibration preserves the original 120° geometry and reduces burr length by 36%. These results demonstrate that ultrasonic vibration effectively reduces damage through multi-scale interactions, offering theoretical guidance for high-precision machining of fiber-reinforced composites. Full article

A ratiometric fluorescent film with high gas sensitivity and stability was developed using electrospinning technology for monitoring food spoilage. 5(6)-Carboxyfluorescein (5(6)-FAM) was used as the indicator, combined with the internal reference Rhodamine B (RHB), to establish a composite ratiometric fluorescent probe (FAM@RHB). The hydrophobic fluorescent films were fabricated by incorporating FAM@RHB probes into polyvinylidene fluoride (PVDF) at varying molar ratios through electrospinning. The FR-2 film with a 2:8 ratio of 5(6)-FAM to RHB exhibited the best performance, demonstrating excellent hydrophobicity with a water contact angle (WCA) of 113.45° and good color stability, with a ΔE value of 2.05 after 14 days of storage at 4 °C. Gas sensitivity tests indicated that FR-2 exhibited a limit of detection (LOD) of 0.54 μM for trimethylamine (TMA). In the application of monitoring the freshness of pork and beef at 4 °C, the fluorescence color of the FR-2 film significantly changed from orange–yellow to green, enabling the visual monitoring of meat freshness. Hence, this study provides a new approach for intelligent food packaging. Full article

Novel bat influenza viruses show different features in contrast to classical influenza A viruses (IAVs). The M2 of IAVs functions as an ion channel that plays an important role in virus entry, viral assembly, and release and also serves as the antiviral target. To date, whether bat influenza M2 functions as the ion channel like classical IAV M2 remains unknown. Here, we show that the bat influenza M2 amino acid at position 31 (N/S) is critical for sensitivity to antivirals targeting the ion channel such as amantadine and other tested antivirals and that the amino acids at position 37 (H/G) and 41 (W/A) are crucial for virus replication and survival. The results indicate that bat influenza M2 functions similarly to conventional IAVs despite the low identity between the two. Full article

Rice is an important crop that significantly contributes to food security. Lodging is considered an important factor limiting rice yield and quality. The objective of this study was to investigate the effects of carbon and nitrogen on lodging in fragrant rice. A 2-year field experiment (2021 to 2022) was conducted with the fragrant rice cultivars Meixiangzhan 2 and Xiangyaxiangzhan grown under nine carbon and nitrogen co-application treatments (CK: 0 mg/L glucose + 0 mg/L urea; T1: 0 mg/L glucose + 50 mg/L urea; T2: 0 mg/L glucose + 100 mg/L urea; T3: 150 mg/L glucose + 0 mg/L urea; T4: 150 mg/L glucose + 50 mg/L urea; T5: 150 mg/L glucose + 100 mg/L urea; T6: 300 mg/L glucose + 0 mg/L urea; T7: 300 mg/L glucose + 50 mg/L urea; and T8: 300 mg/L glucose + 100 mg/L urea). The lodging index and stem characteristics of fragrant rice were investigated. Compared with the CK treatment, the T5 and T7 treatments significantly increased the pushing resistance force by 22.22–127.78% and 50.00–77.50%, respectively. Compared with the other fertilization treatments, the T5 treatment kept the lodging index at a low level and reduced the plant height. The stem characteristics were regulated under the carbon and nitrogen co-application treatments, and the internode length and dry weight significantly influenced the plant height and the pushing resistance force and then regulated the lodging index. Structural equation modeling and random forest modeling analyses suggest that carbon and nitrogen co-application treatments may further improve the resistance of rice to lodging by increasing the dry weight of the third and fourth internodes. Overall, optimized carbon and nitrogen co-application could regulate stem internode morphology and improved lodging resistance. Furthermore, the T5 treatment (150 mg/L glucose + 100 mg/L urea) improved lodging resistance. This study provides guidelines for enhancing lodging resistance by regulating internode characteristics via the co-application of carbon and nitrogen at the booting stage in fragrant rice. Full article

Fraxinus platypoda Oliv. (Oleaceae), an ecologically and economically valuable tree species with key distribution areas in northwestern China, faces conservation challenges due to its fragmented groups and scarce individual numbers. To investigate the genetic consequences of this demographic crisis, we analyzed 65 individuals from 11 natural groups in this region using whole-genome resequencing. We identified a total of 60,503,092 single nucleotide polymorphisms (SNPs), and after further filtering, retained 3,394,299 SNPs for subsequent analysis. Population structure analysis (Neighbor-Joining tree, STRUCTURE, and kinship coefficients) revealed two distinct genetic clusters (K = 2), with principal component analysis (PCA) confirming this subdivision. Cluster I, composed of eight individuals from Groups 3, 5, 8, and 11, is highly differentiated from Cluster II and may be ancestral to it. Among the 11 groups, Groups 3 and 11 show a high genetic diversity and differentiation, with Tajima’s D > 0, indicating a long evolutionary history and balancing selection. The remaining nine groups have a low diversity, low differentiation, and frequent gene flow, with Tajima’s D < 0, suggesting directional selection. A mantel test showed no significant link between genetic variation and geographic isolation (p = 0.460). The high differentiation of Cluster I and gene flow of Cluster II are maintained by factors like evolutionary history and reproductive systems. Groups 3 and 11 are highlighted as important genetic resources deserving priority protection. This study offers key genomic data for conserving fragmented tree species and future adaptability research. Full article

The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies in target recognition and localization. This study compared the performance of multiple You Only Look Once (YOLO) algorithms for recognition and proposed a cluster segmentation method based on K-means++ and a cutting-point localization strategy using geometry-based iterative optimization. A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). A K-means++-based cluster separation algorithm (K = 1~10, optimized via the elbow method) was developed and was combined with OpenCV to iteratively solve the minimum circumscribed triangle vertices. The longest median extension line of the triangle was dynamically determined to be the cutting point. The experimental results demonstrated an average cutting-point deviation of 20 mm and a valid cutting-point ratio of 69.23%. This research provides a robust visual solution for intelligent green Sichuan pepper harvesting equipment, offering both theoretical and engineering significance for advancing the automated harvesting of Sichuan pepper (Zanthoxylum schinifolium) as a specialty economic crop. Full article

The instability of wellbore in hard and brittle shale formations is a key bottleneck constraining the safety and efficiency of drilling engineering. Traditional studies focused on drilling fluid density, particle plugging, and chemical inhibition; however, there is a lack of in-depth analysis on the precise control mechanism of wellbore stability by the rheological properties of drilling fluids. Specifically, while traditional methods are limited in addressing mechanical instability in hard brittle shales with pre-existing micro-fractures, rheological control offers a potential solution by influencing pressure transmission within these fractures. To address this research gap, this study aims to reveal the influence of drilling fluid rheological parameters (specifically viscosity and yield point) on the pressure transmission behavior of the micro-fracture network in hard and brittle shale and to clarify the intrinsic mechanism by which rheological properties stabilize the wellbore. Micro-structure analysis confirmed interconnected micro-fractures (0.5–30 μm). A micro-fracture flow model and simulations evaluated viscosity and yield point effects on pressure transmission. A higher viscosity significantly increased the pressure drop (ΔP) near the wellbore, with limited transmission distance effects. The yield point was minimal. The study reveals that optimizing rheology, particularly increasing viscosity, can suppress pore pressure, reduce collapse pressure, and improve stability. The findings support rheological parameter optimization for safer, economical drilling. In terms of rheological parameter optimization design, this study suggests emphasizing the increase in drilling fluid viscosity to effectively manage wellbore stability in hard brittle shale formations. Full article

Biologically inspired retinal preprocessing improves visual perception by efficiently encoding and reducing entropy in images. In this study, we introduce a new saliency prediction framework that combines a retinal model with deep neural networks (DNNs) using information theory ideas. By mimicking the human retina, our method creates clearer saliency maps with lower entropy and supports efficient computation with DNNs by optimizing information flow and reducing redundancy. We treat saliency prediction as an information maximization problem, where important regions have high information and low local entropy. Tests on several benchmark datasets show that adding the retinal model boosts the performance of various bottom-up saliency prediction methods by better managing information and reducing uncertainty. We use metrics like mutual information and entropy to measure improvements in accuracy and efficiency. Our framework outperforms state-of-the-art models, producing saliency maps that closely match where people actually look. By combining neurobiological insights with information theory—using measures like Kullback–Leibler divergence and information gain—our method not only improves prediction accuracy but also offers a clear, quantitative understanding of saliency. This approach shows promise for future research that brings together neuroscience, entropy, and deep learning to enhance visual saliency prediction. Full article

The 4 × 4.5 m radio telescope array at Guizhou Normal University is an astronomical observation facility in operation, mainly aiming at the scientific detection of pulsars and fast radio bursts. To adequately address the observational requirements of this telescope array, we developed an observation scheduling system. This system is able to predict and plot the elevation change curves of the observed targets in relation to the telescope array during the whole day. In addition, for multiple targets, it provides intelligent scheduling and processing according to the observable time. The system also offers a comprehensive database of calibrators for the flux calibration of the targets. Moreover, it can generate pre-configured array $uv$ coverage maps, which assist in determining the optimal configuration of the array. This system has been operating during the daily observations of the $4\times 4.5$ m radio telescope array and has successfully detected two typical pulsars. It has also been tested for applicability in target-observation prediction in other radio telescope arrays. Full article

To address the critical challenges of wellbore instability in deep coal seam drilling operations, this investigation developed an innovative organic–inorganic composite nanosealing agent (NS) through chemical modification of nano-silica. Advanced characterization techniques including Fourier Transform Infrared Spectroscopy, laser particle size analysis, and Scanning Electron Microscopy revealed that the optimized NS possessed a uniform particle distribution (mean diameter 86 nm) and enhanced surface hydrophobicity, effectively mitigating particle agglomeration. Systematic experimental evaluation demonstrated the material’s multifunctional performance: the NS-enriched drilling fluid achieved an 88.7% reduction in sand bed invasion depth and 76.4% decrease in filtrate loss at optimal concentration. Notably, comparative inhibition tests showed the NS outperformed conventional KCl and KPAM inhibitors, achieving 91.2% shale rolling recovery rate and 65.3% lower swelling rate than deionized water baseline. Core flooding experiments further confirmed superior sealing capability, with 2% NS addition attaining 88% sealing efficiency for low-permeability cores (0.5 mD) and establishing a 10 MPa breakthrough pressure threshold. Field implementation in the SSM1 well at Shenmu Huineng Liangshui Coal Mine validated the technical efficacy, the NS-enhanced drilling fluid system achieved 86.7% coal seam encounter rate with zero wellbore collapse incidents, while core recovery rate improved by 32.6% to 90.4% compared to conventional systems. This research breakthrough provides a scientific foundation for developing next-generation intelligent drilling fluids, demonstrating significant potential for ensuring drilling safety and enhancing gas recovery efficiency in deep coalbed methane reservoirs. Full article

Nuclear Factor of Activated T Cells 5 (NFAT5) is a transcription factor that plays a pivotal role in immune regulation. While its functions have been extensively studied in mammalian immune systems, its role in marine invertebrates, particularly in bivalves, remains largely unexplored. This study provides the first characterization of the NFAT5 gene in the thick-shelled mussel (Mytilus coruscus), investigating its evolutionary characteristics and immunological functions. Using direct RNA sequencing, McNFAT5 was comprehensively analyzed, revealing its critical involvement in the innate immune response of M. coruscus to Vibrio alginolyticus challenge. Differential expression patterns of McNFAT5 were observed across various tissues with the highest expression detected in hemolymphs. The knockdown of McNFAT5 using small interfering RNA (siRNA) led to a significant reduction in the activities of superoxide dismutase (SOD), Na⁺/K⁺-ATPase, and antioxidant enzymes compared to levels observed post-infection. These findings highlight the central role of McNFAT5 in modulating antioxidant defense mechanisms. In conclusion, McNFAT5 is a key regulatory factor in the innate immune system of M. coruscus, providing valuable insights into the immune adaptive mechanisms and evolutionary mechanisms of bivalve immunity. This study contributes to a deeper understanding of the immune regulatory networks in marine invertebrates. Full article

Accurate estimations of forest total carbon storage are essential for understanding ecosystem functioning and improving forest management. This study investigates how multi-source remote sensing data can be used to provide accurate estimations of diameter at breast height (DBH) at the plot level, enhancing biomass estimations across 39.41 × 10⁴ km². The study is focused on Yunnan Province, China, which is characterized by complex terrain and diverse vegetation. Using ground-based survey data from hundreds of plots for model calibration and validation, the methodology combines multi-source remote sensing data, machine learning algorithms, and statistical analysis to develop models for estimating DBH distribution at regional scales. Decision tree showed the best overall performance. The model effectiveness improved when stratified by climatic zones, highlighting the importance of environmental context. Traditional methods based on the kNDVI index had a mean squared error (MSE) of 2575 t/ha and an R² value of 0.69. In contrast, combining model-estimated DBH values with remote sensing data resulted in a substantially lower MSE of 212 t/ha and a significantly improved R² value of 0.97. The results demonstrate that incorporating DBH not only reduced prediction errors but also improved the model’s ability to explain biomass variability. In addition, climatic region classification further increased model accuracy, suggesting that future efforts should consider environmental zoning. Our analyses indicate that water availability during cool and dry periods in this monsoon-influenced region was especially critical in influencing DBH across different subtropical zones. In summary, the study integrates DBH and high-resolution remote sensing data with advanced algorithms for accurate biomass estimation. The findings suggest that this approach can support regional forest management and contribute to research on carbon balance and ecosystem assessment. Full article