Search Results (139)

To develop sustainable strategies for mitigating ruminal methanogenesis and improving nitrogen efficiency in dairy systems, this study investigated how low-dose tannic acid (T), tea polyphenols (TP), and their combination (T+TP; 50:50) modulate rumen microbiota and function. A sample of Holstein cows were given four dietary treatments: (1) control (basal diet); (2) T (basal diet + 0.4% DM tannic acid); (3) TP (basal diet + 0.4% DM tea polyphenols); and (4) T+TP (basal diet + 0.2% DM tannic acid + 0.2% DM tea polyphenols). We comprehensively analyzed rumen fermentation, methane production, nutrient digestibility, milk parameters, and microbiota dynamics. Compared with the control group, all diets supplemented with additives significantly reduced enteric methane production (13.68% for T, 11.40% for TP, and 10.89% for T+TP) and significantly increased milk protein yield. The crude protein digestibility significantly increased in the T group versus control. The results did not impair rumen health or fiber digestion. Critically, microbiota analysis revealed treatment-specific modulation: the T group showed decreased Ruminococcus flavefaciens abundance, while all tannin treatments reduced abundances of Ruminococcus albus and total methanogens. These microbial shifts corresponded with functional outcomes—most notably, the T+TP synergy drove the largest reductions in rumen ammonia-N (34.5%) and milk urea nitrogen (21.1%). Supplementation at 0.4% DM, particularly the T+TP combination, effectively enhances nitrogen efficiency and milk protein synthesis while reducing methane emissions through targeted modulation of key rumen microbiota populations, suggesting potential sustainability benefits linked to altered rumen fermentation. Full article

Human dermcidin (DCD) is synthesized as a 110-amino acid precursor (pre-dermcidin, pre-DCD) containing a 19-residue leader signal sequence, which is removed to produce a leader-less pro-domain-containing peptide termed as pro-dermcidin, pro-DCD. Pro-DCD can be secreted by human eccrine sweat glands and then cleaved into antimicrobial peptides, such as dermcidin (DCD). Emerging evidence suggests that pro-DCD has broader physiological roles beyond antimicrobial defense, potentially serving as a therapeutic agent for inflammatory diseases like sepsis. In this review, we summarize recent evidence supporting pro-DCD as a regulator of innate immunity in sepsis. Full article

Blade tip leakage vortex (TLV) is a critical phenomenon in hydraulic machinery, which can significantly affect the internal flow characteristics and deteriorate the hydraulic performance. In this paper, the blade tip leakage flow and TLV characteristics in a novel dishwasher pump were investigated. The correlation between the vorticity distribution in various directions and the leakage vortices was established within a rotating coordinate system. The results show that the TLV in a composite impeller can be categorized into initial and secondary leakage vortices. The initial leakage vortex originates from the evolution of two corner vortices that initially form at different locations within the blade tip clearance. This vortex induces pressure fluctuations at the impeller inlet; its shedding is identified as the primary contributor to localized energy loss within the flow passage. These findings provide insights into TLVs in complex pump geometries and provide solutions for future pump optimization strategies. Full article

Stable oxygen isotopes in tree rings (δ¹⁸O) serve as important proxies for climate change and offer unique advantages for climate reconstruction in arid and semi-arid regions. We established an annual δ¹⁸O chronology spanning 1964–2023 using Juniperus excelsa tree-ring samples collected from the Alborz Mountains in Iran. We analyzed relationships between δ¹⁸O and key climate variables: precipitation, temperature, Palmer Drought Severity Index (PDSI), vapor pressure (VP), and potential evapotranspiration (PET). Correlation analysis reveals that tree-ring δ¹⁸O is highly sensitive to hydroclimatic variations. Tree-ring cellulose δ¹⁸O shows significant negative correlations with annual total precipitation and spring PDSI, and significant positive correlations with spring temperature (particularly maximum temperature), April VP, and spring PET. The strongest correlation occurs with spring PET. These results indicate that δ¹⁸O responds strongly to the balance between springtime moisture supply (precipitation and soil moisture) and atmospheric evaporative demand (temperature, VP, and PET), reflecting an integrated signal of both regional moisture availability and energy input. The pronounced response of δ¹⁸O to spring evaporative conditions highlights its potential for capturing high-resolution changes in spring climatic conditions. Our δ¹⁸O series remained stable from the 1960s to the 1990s, but showed greater interannual variability after 2000, likely linked to regional warming and climate instability. A comparison with the δ¹⁸O variations from the eastern Alborz Mountains indicates that, despite some differences in magnitude, δ¹⁸O records from the western and eastern Alborz Mountains show broadly similar variability patterns. On a larger climatic scale, δ¹⁸O correlates significantly and positively with the Niño 3.4 index but shows no significant correlation with the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO). This suggests that ENSO-driven interannual variability in the tropical Pacific plays a key role in regulating regional hydroclimatic processes. This study confirms the strong potential of tree-ring oxygen isotopes from the Alborz Mountains for reconstructing hydroclimatic conditions and high-frequency climate variability. Full article

Laser propulsion is a new conceptual technology that drives spacecraft and possesses advantages such as high specific impulse, large payload ratio, and low launch cost. It has potential applications in diverse areas, such as space debris mitigation and removal, microsatellite attitude control, and orbital maneuvering. Liquid pulse laser propulsion has notable advantages among the various laser propulsion systems. We review the concept and the theory of liquid laser propulsion. Then, we categorize the current state of research based on three types of propellants—non-energetic liquids, energetic liquids, and liquid metals—and provide an analysis of the propulsion characteristics arising from the laser ablation of liquids such as water, glycidyl azide polymer (GAP), hydroxylammonium nitrate (HAN), and ammonium dinitramide (ADN). We also discuss future research directions and challenges of pulsed liquid laser propulsion. Although experiments have yielded encouraging outcomes due to the distinctive properties of liquid propellants, continued investigation is essential to ensure that this technology performs reliably in actual aerospace applications. Consistent results under both spatial and ground conditions remain a key research content for fully realizing its potential. Full article

Resistive gas sensors have attracted significant attention due to their simple architecture, low cost, and ease of integration, with widespread applications in environmental monitoring, industrial safety, and healthcare diagnostics. This review provides a comprehensive overview of recent advances in resistive gas sensors, focusing on their fundamental working mechanisms, sensing material design, device architecture optimization, and intelligent system integration. These sensors primarily operate based on changes in electrical resistance induced by interactions between gas molecules and sensing materials, including physical adsorption, charge transfer, and surface redox reactions. In terms of materials, metal oxide semiconductors, conductive polymers, carbon-based nanomaterials, and their composites have demonstrated enhanced sensitivity and selectivity through strategies such as doping, surface functionalization, and heterojunction engineering, while also enabling reduced operating temperatures. Device-level innovations—such as microheater integration, self-heated nanowires, and multi-sensor arrays—have further improved response speed and energy efficiency. Moreover, the incorporation of artificial intelligence (AI) and Internet of Things (IoT) technologies has significantly advanced signal processing, pattern recognition, and long-term operational stability. Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. These synergistic developments are driving resistive gas sensors toward low-power, highly integrated, and multifunctional platforms, particularly in emerging applications such as wearable electronics, breath diagnostics, and smart city infrastructure. This review concludes with a perspective on future research directions, emphasizing the importance of improving material stability, interference resistance, standardized fabrication, and intelligent system integration for large-scale practical deployment. Full article

Existing studies on backdoor attacks in large language models (LLMs) have contributed significantly to the literature by exploring trigger-based strategies—such as rare tokens or syntactic anomalies—that, however, limit both their stealth and generalizability, rendering them susceptible to detection. In this study, we propose HDPAttack, a novel hidden backdoor prompt attack method which is designed to overcome these limitations by leveraging the semantic and structural properties of prompts as triggers rather than relying on explicit markers. Not symmetric to traditional approaches, HDPAttack injects carefully crafted fake demonstrations into the training data, semantically re-expressing prompts to generate examples that exhibit high consistency in input semantics and corresponding labels. This method guides models to learn latent trigger patterns embedded in their deep representations, thereby enabling backdoor activation through natural language prompts without altering user inputs or introducing conspicuous anomalies. Experimental results across datasets (SST-2, SMS, AGNews, Amazon) reveal that HDPAttack achieved an average attack success rate of 99.87%, outperforming baseline methods by 2–20% while incurring a classification accuracy loss of ≤1%. These findings set a new benchmark for undetectable backdoor attacks and underscore the urgent need for advancements in prompt-based defense strategies. Full article

This study aims to elucidate the mechanisms by which tip seal leakage flow induces steam excitation, thereby enhancing the operational safety of steam turbines. Using numerical simulations, it investigates the detailed characteristics of the flow field in the turbine tip seal cavity. By introducing Boundary Vorticity Flux (BVF) into the tip seal flow field, this research explores the relationship between leakage vortex structures in non-uniform flow fields at the blade tip and the resulting steam excitation forces. The results demonstrate that, during eccentric rotor operation, the extent and intensity of vortices within the seal cavity vary, lead to changes in the BVF distribution along the shroud surface, which in turn alter the tangential forces and induce variations in lateral excitation force at the blade tip. Additionally, the non-uniform flow in the tip seal clearance induces circumferential pressure variations across the shroud, leading to adjustments in radial excitation force at the blade tip. Full article

Defects are a direct cause of failure in ferromagnetic components, which can be evaluated via electromagnetic non-destructive testing (ENDT) methods. However, the existing studies exhibit several limitations (e.g., inaccurate quantification, over-reliance on algorithms, and non-intuitive result presentation, among others) in quantitative defect evaluation. To accurately describe the quantitative relationship between ENDT signals and defect dimensional parameters, the electromagnetic model and electromagnetic induction model are introduced in this paper to elucidate the physical mechanism of ENDT, as both models provide a basis for the selection of the constitutive relationship for simulation analysis. Then, a higher precision three-dimensional nonlinear finite element simulation model is established, and the effects of the excitation parameters and detection positions on signal characteristics are investigated. The simulation results indicate that the excitation frequency influences both the detection depth and sensitivity of ENDT, while the voltage amplitude affects the peak strength of the magnetic signal. Consequently, the excitation parameters are determined to be a 10 Hz frequency with a 25 V amplitude. Based on the characterization of signal peaks at positions of 0°, 90°, 180°, and 270°, the characteristic parameter K_A of the peak electrical signal curve is proposed as a quantitative index for evaluating defects. The quantitative experimental results show that K_A is related to the defect dimension. Specifically, the K_A value monotonically decreases from a constant greater than 1 to a constant less than 1 as the defect length increases, K_A is positively correlated with the defect width, and K_A follows a parabolic trend (first increase and then decrease) as the defect depth increases. Notably, K_A values associated with defect width and depth remain below 1. All the above findings provide a basis for evaluating defect dimensions. The results of this paper provide a novel ENDT method for evaluating defects, which is of great significance for improving the accuracy of ENDT and promoting its engineering applications. Full article

This study investigated the correlation between thirteen inorganic elements, five key bioactive compounds, and environmental factors in Polygonum perfoliatum L. from fifteen different origins. Analyses were conducted using techniques such as ultrasound-assisted extraction, HPLC, ICP-AES, PCA, and HCA. The results indicate that the geographical origin significantly influences the contents of inorganic elements and bioactive compounds in Polygonum perfoliatum L., and a certain correlation exists among elements, compounds, and environmental factors. This research provides a theoretical foundation for the development and utilization of Polygonum perfoliatum L. Full article

How to further improve the fuel economy and emission performance of hybrid vehicles through scientific and reasonable energy management strategies has become an urgent issue to be addressed at present. This paper proposes an energy management model based on speed prediction using Long Short-Term Memory (LSTM) neural networks. The initial learning rate and dropout probability of the LSTM speed prediction model are optimized using a Double Deep Q-Network (DDQN) algorithm. Furthermore, the LSTM speed prediction function is implemented within a Model Predictive Control (MPC) framework. A fuzzy logic-based driving mode recognition system classifies driving cycles and identifies real-time conditions. The fuzzy logic-based driving mode is used to divide the typical driving cycle into different driving modes, and the real-time driving modes are identified. The LSTM-MPC method achieves low RMSE across different prediction horizons. Using predicted power demand, battery SOC, and real-time power demand as inputs, the model implements MPC for real-time control. In our experiments, four prediction horizons (5 s, 10 s, 15 s, and 20 s) were set. The energy management strategy demonstrated optimal performance and the lowest fuel consumption at a 5 s horizon, with fuel usage at only 6.3220 L, saving 2.034 L compared to the rule-based strategy. Validation under the UDDS driving cycle revealed that the LSTM-MPC-DDQN strategy reduced fuel consumption by 0.2729 L compared to the rule-based approach and showed only a 0.0749 L difference from the DP strategy. Full article

Tire force is a critical state parameter for vehicle dynamics control systems during vehicle operation. Compared with tire force estimation methods relying on vehicle dynamics or tire models, intelligent tire technology can provide real-time feedback regarding tire–road interactions to the vehicle control system. To address the demand for accurate tire force prediction in active safety control systems under various operating conditions, this paper proposes an intelligent tire force estimation method, integrating sensor-measured dynamic response parameters and machine learning techniques. A 205/55 R16 radial tire was selected as the research object, and a finite element model was established using the parameterized modeling approach with the ABAQUS finite element simulation software. The validity of the finite element model was verified through indoor static contact and stiffness tests. To investigate the sensitive response areas and variables associated with tire force, the ground deformation area of the inner liner was refined along the transverse and circumferential directions. Variance-based global sensitivity analysis combined with dimensional reduction methods was used to evaluate the sensitivity of acceleration, strain, and displacement responses to variations in longitudinal and lateral forces. Based on the results of the global sensitivity analysis, the influence of longitudinal and lateral forces on sensitive response variables in their respective sensitive response areas was examined, and characteristic values of the corresponding response signal curves were analyzed and extracted. Three intelligent tire force estimation models with different sensor-targeting configurations were established using radial basis function (RBF) neural networks. The mean relative error (MRE) of intelligent tire force estimation for these models remained within 10%, with Model 3 demonstrating an MRE of less than 2% and estimation errors of 1.42% and 1.10% for longitudinal and lateral forces, respectively, indicating strong generalization performance. The results show that tire forces exhibit high sensitivity to acceleration and displacement responses in the crown and sidewall areas, providing methodological guidance for the targeted sensor configuration in intelligent tires. The intelligent tire force estimation method based on the RBF neural network effectively achieves accurate estimation, laying a theoretical foundation for the advancement of vehicle intelligence and technological innovation. Full article

The tire grounding parameters are a crucial component of the vehicle dynamics control system; accurate acquisition of grounding parameters is important for improving traction, braking force, and handling stability during vehicle operation. This paper studies strain-based intelligent tire contact patch length and vertical force estimation; first, a 205/55R16 radial tire was established, and static grounding experiments were carried out to verify the validity of the finite element model. Second, the sensitivity of the circumferential strain signal of the inner liner in the contact area of a tire with complex tread patterns was discussed. Methods for estimating the contact angle and contact patch length of rolling tires were established, and the estimation accuracy under different tire parameters and operating conditions were analyzed. Finally, the vertical force-sensitive response characteristics were analyzed and extracted, and the vertical force prediction model of a radial tire based on particle swarm optimization BP neural network was established. Full article

This study introduces a method for predicting the tensile properties of high-strength steel/carbon fiber-reinforced polymer (CFRP) composite laminates using Metal Volume Fraction (MVF) theory. DP590 and DP980 high-strength steels (thickness ~0.8 mm) were selected as substrates, and composite laminates were fabricated by compression molding with CFRP prepreg. Tensile tests were performed on an MTS universal testing machine, and fracture morphology was analyzed using scanning electron microscopy (SEM). The results demonstrated a typical mixed failure mode: necking and fracture in the metal layer, and neat fiber fracture in the CFRP layer. Comparisons of experimental tensile strength with theoretical predictions revealed that the model based on the metal strength at fracture significantly outperformed the model using tensile strength for predictions, with narrower error ranges. For example, the error for DP590/CFRP laminates ranged from 2.31% to 12.89%, whereas for DP980/CFRP laminates, it was –6.12%. Additionally, the study showed that using metals with higher plasticity in fiber metal laminates could underutilize the metal layer’s potential at peak stress, leading to significant deviations when predictions rely on tensile strength. Therefore, it is recommended to use the metal strength corresponding to peak stress for more accurate MVF-based tensile property predictions. This method provides a robust theoretical foundation for predicting the tensile performance of high-strength steel/CFRP laminates, aiding in optimizing structural designs for automotive and aerospace applications. Future research could explore the effects of different metal and fiber combinations, as well as more complex stacking designs. Full article

Object detection is essential for the perception systems of intelligent driving vehicles. RT-DETR has emerged as a prominent model. However, its direct application in intelligent driving vehicles still faces issues with the misdetection of occluded or small targets. To address these challenges, we propose a High-Precision Real-Time object detection algorithm (HPRT-DETR). We designed a Basic-iRMB-CGA (BIC) Block for a backbone network that efficiently extracts features and reduces the model’s parameters. We thus propose a Deformable Attention-based Intra-scale Feature Interaction (DAIFI) module by combining the Deformable Attention mechanism with the Intra-Scale Feature Interaction module. This enables the model to capture rich semantic features and enhance object detection accuracy in occlusion. The Local Feature Extraction Fusion (LFEF) block was created by integrating the local feature extraction module with the CNN-based Cross-scale Feature Fusion (CCFF) module. This integration expands the model’s receptive field and enhances feature extraction without adding learnable parameters or complex computations, effectively minimizing missed detections of small targets. Experiments on the KITTI dataset show that, compared to RT-DETR, HPRT-DETR improves mAP50 and FPS by 1.98% and 15.25%, respectively. Additionally, its generalization ability is assessed on the SODA 10M dataset, where HPRT-DETR outperforms RT-DETR in most evaluation metrics, confirming the model’s effectiveness. Full article