Search Results (246)

The Qiangtang Basin (Tibetan Plateau) poses significant geophysical challenges for seismic exploration due to near-surface widespread permafrost and steeply dipping Mesozoic strata induced by the Cenozoic Indo-Eurasian collision. These seismic geological conditions considerably contribute to lower signal-to-noise ratios (SNRs) with complex wavefields, to some extent reducing the reliability of conventional seismic imaging and structural interpretation. To address this, the common-reflection-surface (CRS) stack method, derived from optical paraxial ray theory, is implemented to transcend horizontal layer model constraints, offering substantial improvements in high-SNR prestack gather generation and prestack depth migration (PSDM) imaging, notably for permafrost zones. Using 2D seismic data from the basin, we detailedly compare the CRS stack with conventional SNR enhancement techniques—common midpoint (CMP) FlexBinning, prestack random noise attenuation (PreRNA), and dip moveout (DMO)—evaluating both theoretical foundations and practical performance. The result reveals that CRS-processed prestack gathers yield superior SNR optimization and signal preservation, enabling more robust PSDM velocity model building, while comparative imaging demonstrates enhanced diffraction energy—particularly at medium (20–40%) and long (40–60%) offsets—critical for resolving faults and stratigraphic discontinuities in PSDM. This integrated validation establishes CRS stacking as an effective preprocessing foundation for the depth-domain imaging of complex permafrost geology, providing critical improvements in seismic structural resolution and reduced interpretation uncertainty for hydrocarbon exploration in permafrost-bearing basins. Full article

Intrauterine inflammation (IUI) is involved in the development of bronchopulmonary dysplasia (BPD). Previously, we observed BPD-like pathological changes in a mouse model of IUI. This study aimed to identify the key molecules involved in IUI-induced lung injury, focusing on NR4A1. Pregnant C57BL/6 mice were randomly divided into control and IUI groups. To verify the intervention effects, Nr4a1 siRNA was administered intranasally on postnatal day 3, while an NR4A1 overexpression plasmid was applied in MLE-12 cells to investigate downstream molecules. We found that the lungs of IUI-induced offspring exhibited a simplified structure on postnatal day 1 and excessive collagen fiber deposition by day 90. Postnatal NR4A1 intervention reversed IUI-induced neonatal lung injury. NR4A1 overexpression reduced cell proliferation and AKT and ERK1/2 phosphorylation levels, while also affecting the expression of the key epithelial–mesenchymal transition (EMT)-related gene TGF-β. EREG is a downstream target with potential NR4A1 binding sites in its promoter region. The expression of EMT-related genes can be recovered by blocking the receptor of EREG. Our findings imply that IUI induces BPD-like lung injury in neonates and fibrosis-like lung lesions in adult mice. The NR4A1-EREG-EGFR signaling pathway in pulmonary epithelial cells is crucial in IUI-induced lung injury, highlighting a key therapeutic target for mitigating BPD-like injury. Full article

Since the discovery of two-dimensional transition metal carbides and nitrides (MXenes), MXenes have attracted widespread research in the academic community due to their advantages, such as adjustable interlayer spacing, excellent hydrophilicity, conductivity, compositional diversity, and rich surface chemical composition. More than 100 different MXene combinations can be calculated theoretically, but only more than 40 have been successfully synthesized through experiments. Among the many synthesized and reported MXene materials, vanadium-based carbide MXenes, represented by V₂CT_x and V₄C₃T_x, show excellent application prospects in energy storage and have become the focus of researchers. In this review, we mainly discuss the structure, characteristics, and preparation methods of vanadium-based MXene precursors in the MAX phase and their applications in supercapacitors. Finally, we propose the main challenges existing at the current stage of vanadium-based materials and their heterostructures and provide a perspective on future research directions. Full article

Frequency-modulated continuous-wave (FMCW) LiDAR systems frequently experience noise interference during multi-target measurements in real-world applications, resulting in target overlapping and diminished detection accuracy. Conventional denoising approaches—such as Empirical Mode Decomposition (EMD) and wavelet thresholding—are often constrained by challenges like mode mixing and the attenuation of weak target signals, which limits their detection precision. To address these limitations, this study presents a novel denoising framework that integrates an optimized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm and singular value decomposition (SVD). The CEEMDAN algorithm’s two critical parameters—the noise standard deviation and the number of noise additions—are optimally determined using particle swarm optimization (PSO), with the envelope entropy of the intrinsic mode functions (IMFs) serving as the fitness criterion. IMFs are subsequently selected based on spectral and amplitude comparisons with the original signal to facilitate initial signal reconstruction. Following CEEMDAN-based decomposition, SVD is employed with a normalized soft thresholding technique to further suppress residual noise. Validation using both synthetic and experimental datasets demonstrates the superior performance of the proposed approach over existing methods in multi-target scenarios. Specifically, it reduces the root mean square error (RMSE) by 45% to 59% and the mean square error (MSE) by 34% to 69%, and improves the signal-to-noise ratio (SNR) by 1.85–4.38 dB and the peak signal-to-noise ratio (PSNR) by 1.18–6.94 dB. These results affirm the method’s effectiveness in enhancing signal quality and target distinction in noisy FMCW LiDAR measurements. Full article

Aerial tracking is an important service for many Unmanned Aerial Vehicle (UAV) applications. Existing work has failed to provide robust solutions when handling target disappearance, viewpoint changes, and tracking drifts in practical scenarios with limited UAV resources. In this paper, we propose a closed-loop framework integrating three key components: (1) a lightweight adaptive detection with multi-scale feature extraction, (2) spatiotemporal motion modeling through Kalman-filter-based trajectory prediction, and (3) autonomous decision-making through composite scoring of detection confidence, appearance similarity, and motion consistency. By implementing dynamic detection-tracking coordination with quality-aware feature preservation, our system enables real-time operation through performance-adaptive frequency modulation. Evaluated on VOT-ST2019 and OTB100 benchmarks, the proposed method yields marked improvements over baseline trackers, achieving a 27.94% increase in Expected Average Overlap (EAO) and a 10.39% reduction in failure rates, while sustaining a frame rate of 23–95 FPS on edge hardware. The framework achieves rapid target reacquisition during prolonged occlusion scenarios through optimized protocols, outperforming conventional methods in sustained aerial surveillance tasks. Full article

This study addresses the microbial corrosion of cement-based materials in coastal urban sewer networks, systematically investigating the kinetic mechanisms of sulfur biogeochemical cycling under seawater infiltration conditions. Through dynamic monitoring of sulfide concentrations and environmental parameter variations in anaerobic pipelines, a multiphase coupled kinetic model integrating liquid-phase, gas-phase, and biofilm metabolic processes was developed. The results demonstrate that moderate salinity enhances the activity of sulfate-reducing bacteria (SRB) and accelerates sulfate reduction rates, whereas excessive sulfide accumulation inhibits SRB activity. At 35 °C, the mathematical model coefficient “a” for sulfate reduction in the reactor with 3 g/L salinity was significantly higher than those in reactors with 19 g/L and 35 g/L salinities, with no significant difference observed between the latter two. Overall, high sulfate concentrations do not act as limiting factors for sulfide oxidation under anaerobic conditions; instead, they enhance the reaction within specific concentration ranges. The refined kinetic model enables prediction of sulfur speciation in tropical coastal urban sewer pipelines, providing a scientific basis for corrosion risk assessment. Full article

This study investigates the deformation characteristics and base stability of a circular diaphragm wall support system (external diameter: 90 m, wall thickness: 1.5 m) with pit bottom reinforcement for the South Anchorage deep foundation pit of the Zhangjinggao Yangtze River Bridge, which uses layered and partitioned top-down excavation combined with lining construction. Through field monitoring (deep horizontal displacement of the diaphragm wall, vertical displacement at the wall top, and earth pressure) and numerical simulations (PLAXIS Strength Reduction Method), we systematically analyzed the deformation evolution and failure mechanisms during construction. The results indicate the following: (1) Under the synergistic effect of the circular diaphragm wall, lining, and pit bottom reinforcement, the maximum horizontal displacement at the wall top was less than 30 mm and the vertical displacement was 0.04%H, both significantly below code-specified thresholds, verifying the effectiveness of the support system and pit bottom reinforcement. (2) Earth pressure exhibited a “decrease-then-increase” trend during the excavation proceeds. High-pressure jet grouting pile reinforcement at the pit base significantly enhanced basal constraints, leading to earth pressure below the Rankine active limit during intermediate stages and converging toward theoretical values as deformation progressed. (3) Without reinforcement, hydraulic uplift failure manifested as sand layer suspension and soil shear. After reinforcement, failure modes shifted to basal uplift and wall-external soil sliding, demonstrating that high-pressure jet grouting pile reinforcement had positive contribution basal heave stability by improving soil shear strength. (4) Improved stability verification methods for anti-heave and anti-hydraulic-uplift were proposed, incorporating soil shear strength contributions to overcome the underestimation of reinforcement effects in traditional pressure equilibrium and Terzaghi bearing capacity models. This study provides theoretical and practical references for similar deep foundation pit projects and offers systematic solutions for the safety design and deformation characteristics of circular diaphragm walls with pit bottom reinforcement. Full article

Modular multiplication is a pivotal operation in public-key cryptosystems such as RSA, ElGamal, and ECC. Modular multiplication design is crucial for improving overall system performance due to the large-bit-width operation with high computational complexity. This paper provides a classification of integer multiplication algorithms based on their implementation principles. Furthermore, the core concepts, implementation challenges, and research advancements of multiplication algorithms are systematically summarized. This paper also gives a brief overview of modular reduction algorithms for various types of moduli and discusses the implementation principles, application scenarios, and current research results. Finally, the detailed research development of modular multiplication algorithms in four major classes over prime fields is deeply analyzed and summarized, making it essential as a guide for future research. Full article

Background and Objectives: Gliomas are characterized by high disability rates, frequent recurrence, and low survival rates, posing a significant threat to human health. Accurate grading of gliomas is crucial for treatment plan selection and prognostic assessment. Previous studies have primarily focused on the binary classification (i.e., high grade vs. low grade) of gliomas. In order to perform the four-grade (grades I, II, III, and IV) glioma classification preoperatively, we constructed an artificial neural network (ANN) model using magnetic resonance imaging data. Materials and Methods: We reviewed and included patients with gliomas who underwent preoperative MRI examinations. Radiomics features were derived from contrast-enhanced T1-weighted images (CE-T₁WI) using Pyradiomics and were selected based on their Spearman’s rank correlation with glioma grades. We developed an ANN model to classify the four pathological grades of glioma, assigning training and validation sets at a 3:1 ratio. A diagnostic confusion matrix was employed to demonstrate the model’s diagnostic performance intuitively. Results: Among the 362-patient cohort, the ANN model’s diagnostic performance plateaued after incorporating the first 19 of the 530 extracted radiomic features. At this point, the average overall diagnostic accuracy ratings for the training and validation sets were 91.28% and 87.04%, respectively, with corresponding coefficients of variation (CVs) of 0.0190 and 0.0272. The diagnostic accuracies for grades I, II, III, and IV in the training set were 91.9%, 89.9%, 92.1%, and 90.7%, respectively. The diagnostic accuracies for grades I, II, III, and IV in the validation set were 88.7%, 87.1%, 86.5%, and 86.9%, respectively. Conclusions: The MRI radiomics-based ANN model shows promising potential for the four-type classification of glioma grading, offering an objective and noninvasive method for more refined glioma grading. This model could aid in clinical decision making regarding the treatment of patients with various grades of gliomas. Full article

Milk fat is an important nutritional component and flavor substance in dairy products. Its content and composition directly affect the nutritional value, processing characteristics, and economic benefits of dairy products. Therefore, an in-depth exploration of the molecular mechanisms that influence milk protein synthesis holds profound significance for dairy farming and dairy production. Molecular biology techniques were used to construct CDKN1A overexpression and interference vectors. Using BMECs (bovine mammary epithelial cells) as the experimental model, the vectors were transfected into the cells via liposome mediation to investigate the effect of CDKN1A on the expression of genes related to milk protein synthesis. The results showed that the CDKN1A overexpression and interference vectors were successfully constructed, and the overexpression of CDKN1A reduced milk protein synthesis, and the interference of CDKN1A enhanced milk protein synthesis. This finding provides an important theoretical basis for dairy farming and dairy production. By regulating the expression level of CDKN1A, it is possible to achieve precise control of milk protein yield in dairy cows. It also offers a potential target for the development of new feed additives or drugs. These additives or drugs can promote milk protein synthesis by regulating the activity of CDKN1A, providing new strategies and methods for the sustainable development of the dairy industry. Full article

In the field of enhanced oil recovery (EOR), particularly for water control in high-temperature reservoirs, there is a critical need for effective in-depth water shutoff and conformance control technologies. Polymer-based in situ-cross-linked gels are extensively employed for enhanced oil recovery (EOR), yet their short gelation time under high-temperature reservoir conditions (e.g., >120 °C) limits effective in-depth water shutoff and conformance control. To address this, we developed a hydrogel system via the in situ cross-linking of polyacrylamide (PAM) with phenolic resin (PR), reinforced by silica sol (SS) nanoparticles. We employed a variety of research methods, including bottle tests, viscosity and rheology measurements, scanning electron microscopy (SEM) scanning, density functional theory (DFT) calculations, differential scanning calorimetry (DSC) measurements, quartz crystal microbalance with dissipation (QCM-D) measurement, contact angle (CA) measurement, injectivity and temporary plugging performance evaluations, etc. The composite gel exhibits an exceptional gelation period of 72 h at 130 °C, surpassing conventional systems by more than 4.5 times in terms of duration. The gelation rate remains almost unchanged with the introduction of SS, due to the highly pre-dispersed silica nanoparticles that provide exceptional colloidal stability and the system’s pH changing slightly throughout the gelation process. DFT and SEM results reveal that synergistic interactions between organic (PAM-PR networks) and inorganic (SS) components create a stacked hybrid network, enhancing both mechanical strength and thermal stability. A core flooding experiment demonstrates that the gel system achieves 92.4% plugging efficiency. The tailored nanocomposite allows for the precise management of gelation kinetics and microstructure formation, effectively addressing water control and enhancing the plugging effect in high-temperature reservoirs. These findings advance the mechanistic understanding of organic–inorganic hybrid gel systems and provide a framework for developing next-generation EOR technologies under extreme reservoir conditions. Full article

Aiming to address the problems of fewer related studies on autonomous navigation algorithms based on multi-sensor fusion in complex scenarios in pastures, lower degrees of fusion, and insufficient cruising accuracy of the operation path in complex outdoor environments, a multimodal autonomous navigation system is proposed based on a loosely coupled architecture of Cartographer–RTAB-Map (real-time appearance-based mapping). Through laser-vision inertial guidance multi-sensor data fusion, the system achieves high-precision mapping and robust path planning in complex scenes. First, comparing the mainstream laser SLAM algorithms (Hector/Gmapping/Cartographer) through simulation experiments, Cartographer is found to have a significant memory efficiency advantage in large-scale scenarios and is thus chosen as the front-end odometer. Secondly, a two-way position optimization mechanism is innovatively designed: (1) When building the map, Cartographer processes the laser with IMU and odometer data to generate mileage estimations, which provide positioning compensation for RTAB-Map. (2) RTAB-Map fuses the depth camera point cloud and laser data, corrects the global position through visual closed-loop detection, and then uses 2D localization to construct a bimodal environment representation containing a 2D raster map and a 3D point cloud, achieving a complete description of the simulated ranch environment and material morphology and constructing a framework for the navigation algorithm of the pushing robot based on the two types of fused data. During navigation, the combination of RTAB-Map’s global localization and AMCL’s local localization is used to generate a smoother and robust positional attitude by fusing IMU and odometer data through the EKF algorithm. Global path planning is performed using Dijkstra’s algorithm and combined with the TEB (Timed Elastic Band) algorithm for local path planning. Finally, experimental validation is performed in a laboratory-simulated pasture environment. The results indicate that when the RTAB-Map algorithm fuses with the multi-source odometry, its performance is significantly improved in the laboratory-simulated ranch scenario, the maximum absolute value of the error of the map measurement size is narrowed from 24.908 cm to 4.456 cm, the maximum absolute value of the relative error is reduced from 6.227% to 2.025%, and the absolute value of the error at each location is significantly reduced. At the same time, the introduction of multi-source mileage fusion can effectively avoid the phenomenon of large-scale offset or drift in the process of map construction. On this basis, the robot constructs a fusion map containing a simulated pasture environment and material patterns. In the navigation accuracy test experiments, our proposed method reduces the root mean square error (RMSE) coefficient by 1.7% and Std by 2.7% compared with that of RTAB-MAP. The RMSE is reduced by 26.7% and Std by 22.8% compared to that of the AMCL algorithm. On this basis, the robot successfully traverses the six preset points, and the measured X and Y directions and the overall position errors of the six points meet the requirements of the pasture-pushing task. The robot successfully returns to the starting point after completing the task of multi-point navigation, achieving autonomous navigation of the robot. Full article

The increasing integration of distributed photovoltaics (PVs) has intensified voltage violations in active distribution networks (ADNs). Traditional centralized voltage regulation approaches face substantial challenges in terms of communication and computation. Distributed control methods can help mitigate these issues through distributed algorithms but struggle to track real-time fluctuations in PV generation. Local control offers fast voltage adjustments but lacks coordination among different PV units. This paper presents a hierarchical distributed and local voltage control strategy for PV clusters. First, the alternating direction method of multipliers (ADMM) algorithm is adopted to coordinate the reactive power outputs of PV inverters across clusters, providing reference values for local control. Then, in the local control phase, a Q-P control strategy is utilized to address real-time PV fluctuations. The flexibility of the local control strategy is enhanced using the lifted linear decision rule, enabling a rapid response to PV power fluctuations. Finally, the proposed strategy is tested on both the modified IEEE 33-node distribution system and a practical 53-node distribution system to evaluate its performance. The results demonstrate that the proposed method effectively mitigates voltage issues, reducing the average voltage deviation by 53.93% while improving flexibility and adaptability to real-time changes in PV output. Full article

Modern aero-engines need alloys that sustain both strength and ductility at high temperatures. However, conventional titanium alloys face inherent trade-offs between strength and ductility. In situ TiB-reinforced titanium matrix composites could fill this gap, but their texture evolution and hot-working mechanics are still poorly understood. In this study, TiB-reinforced IMI834 titanium matrix composites were synthesized using in situ technology in a remelting furnace. Meanwhile, the evolution of microstructure and texture in the hot-rolled titanium matrix composites was examined through both Abaqus simulations and experimental observations. Results indicate that dynamic recrystallization occurred in the microstructure of the composites at a deformation level of 95%. Due to the specific orientation relationship between the TiB whiskers and Ti matrix, the hot-rolled composites developed a pronounced [11-20]_Ti // rolling direction fiber texture. TiB whiskers rotated toward the rolling direction, enhancing the intensity of the [11-20]_Ti // rolling direction fiber texture, consistent with the predictions from numerical simulations. Tensile tests revealed that the combined effects of grain refinement and the rotation of TiB whiskers along the rolling direction increased the yield strength of the hot-rolled composite to 1153 MPa, while simultaneously raising the elongation to 10%. Full article

The Hetao Plain Irrigation District of Inner Mongolia faces critical agricultural sustainability challenges due to its arid climate, exacerbated by tightening Yellow River water allocations and pervasive water inefficiencies in the current wheat cultivation practices. This study addresses water scarcity by evaluating the impact of regulated deficit irrigation strategies on spring wheat production, with the dual objectives of enhancing water conservation and optimizing yield–quality synergies. Through a two-year field experiment (2020~2021), four irrigation regimes were implemented: rain-fed control (W0), single irrigation at the tillering–jointing stage (W1), dual irrigation at the tillering–jointing and heading–flowering stages (W2), and triple irrigation incorporating the grain-filling stage (W3). A comprehensive analysis revealed that an incremental irrigation frequency progressively enhanced plant morphological traits (height, upper three-leaf area), population dynamics (leaf area index, dry matter accumulation), and physiological performance (flag leaf SPAD, net photosynthetic rate), all peaking under the W2 and W3 treatments. While yield components and total water consumption exhibited linear increases with irrigation inputs, grain yield demonstrated a parabolic response, reaching maxima under W2 (29.3% increase over W0) and W3 (29.1%), whereas water use efficiency (WUE) displayed a distinct inverse trend, with W2 achieving the optimal balance (4.6% reduction vs. W0). The grain quality parameters exhibited divergent responses: the starch content increased proportionally with irrigation, while protein-associated indices (wet gluten, sedimentation value) and dough rheological properties (stability time, extensibility) peaked under W2. Notably, protein content and its subcomponents followed a unimodal pattern, with the W0, W1, and W2 treatments surpassing W3 by 3.4, 11.6, and 11.3%, respectively. Strong correlations emerged between protein composition and processing quality, while regression modeling identified an optimal water consumption threshold (3250~3500 m³ ha⁻¹) that concurrently maximized grain yield, protein output, and WUE. The W2 regime achieved the synchronization of water conservation, yield preservation, and quality enhancement through strategic irrigation timing during critical growth phases. These findings establish a scientifically validated framework for sustainable, intensive wheat production in arid irrigation districts, resolving the tripartite challenge of water scarcity mitigation, food security assurance, and processing quality optimization through precision water management. Full article