Search Results (2,139)

This paper explores the role of one-part alkali-activated cement, utilizing metakaolin as a precursor, in the long-term stabilization of mining tailings. Investigating three key factors (Si/Al and Na/Si ratios and curing period), this study reveals insights into the mechanical performance and microstructure of alkali-activated cemented iron ore tailings. Unconfined compressive strength test, statistical analysis, and Scanning Electron Microscopy analysis with Energy Dispersive Spectroscopy were performed. Findings indicate that the Si/Al ratio significantly influences strength, with an optimal ratio of 3.5. The Na/Si ratio introduces complexity, affecting alkali availability and reactivity, leading to nuanced strength variations. Extended curing periods consistently enhance the strength of alkali-activated cement, highlighting its dynamic nature. Notably, the 7-day specimens exhibit a less homogeneous distribution, weaker bonding, and decreased structural integrity compared to their 60-day counterparts. This research underscores the intricate nature of alkali-activated cement hydration, emphasizing the interdependence of Si/Al and Na/Si ratios. The observed strengthening effect with prolonged curing suggests the potential for tailoring these materials to specific applications. Addressing a research gap, especially in applying alkali-activation to mining tailings stabilization, this study highlights metakaolin’s role as a suitable precursor. Full article

Coumarins are known for interacting with proteins and exhibiting diverse biological activities. This study investigates the interaction between coumarin-343 (C343) and human (HSA) and bovine (BSA) serum albumins. Fluorescence spectroscopy and theoretical simulations, including density functional theory (DFT) and molecular docking, were used to analyze the ligand–protein complex formation. The fluorescence quenching data revealed that C343 binds to both proteins, with binding constants of 2.1 × 10⁵ mol·L⁻¹ (HSA) and 6.5 × 10⁵ mol·L⁻¹ (BSA), following a 1:1 stoichiometry. Binding site markers identified drug site I (DS1), located in subdomain IIA, as the preferential binding region for both proteins. Computational results supported these findings, showing high affinity for DS1, with binding energies of −69.02 kcal·mol⁻¹ (HSA) and −67.22 kcal·mol⁻¹ (BSA). While complex formation was confirmed for both proteins, differences emerged in the induced circular dichroism (ICD) signals. HSA displayed a distinct ICD profile compared to BSA in both intensity and absorption maximum. Molecular Docking revealed that the C343 conformation differed between HSA and BSA, explaining the variation in ICD signals. These results highlight the importance of protein structure in modulating ligand interactions and spectral responses. Full article

Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies. Full article

Marine coral sand–clay mixtures (MCCM) are promising green fill materials in civil engineering projects, where their strength characteristics play a vital role in ensuring structural safety and stability. To investigate these properties, a series of triaxial shear tests were performed under diverse conditions, including variations in asperity spacing, asperity height, the number of reinforcement layers, confining pressure, and axial strain. This experimental campaign yielded a robust strength dataset for MCCM. Utilizing this dataset, several predictive models were developed, including a standard Support Vector Machine (SVM), an SVM optimized via Genetic Algorithm (GA-SVM), an SVM enhanced by Particle Swarm Optimization (PSO-SVM), and a hybrid model incorporating Logical Development Algorithm preprocessing a SVM model (LDA-SVM). Among these models, the LDA-SVM model exhibited the best performance, achieving a test RMSE of 1.67245 and a correlation coefficient (R) of 0.996, demonstrating superior prediction accuracy and strong generalization ability. Sensitivity analyses revealed that asperity spacing, asperity height, and confining pressure are the most influential factors affecting MCCM strength. Moreover, an explicit empirical equation was derived from the LDA-SVM model, allowing practitioners to estimate strength without relying on complex machine learning tools. The results of this study offer practical guidance for the optimized design and safety evaluation of MCCM in civil engineering applications. Full article

Monsoon evergreen broad-leaved forests (MEBFs) represent one of the most species-rich and structurally complex vegetation types, and one of the most widely distributed forests in Yunnan Province, Southwest China. However, they have yet to undergo a comprehensive analysis on their community diversity, spatial differentiation patterns, and underlying drivers across Yunnan. Based on extensive field surveys during 2021–2024 with 548 MEBF plots, this study employed the Unweighted Pair Group Method for forest community classification and Non-metric Multidimensional Scaling for ordination and interpretation of community–environment association. A total of 3517 vascular plant species were recorded in the plots, including 1137 tree species, 1161 shrubs, and 1219 herbs. Numerical classification divided the plots into 3 alliance groups and 24 alliances: (1) Castanopsis–Schima (Lithocarpus) Forest Alliance Group (16 alliances), predominantly distributed west of 102°E in central-south and southwest Yunnan; (2) Castanopsis–Machilus (Beilschmiedia) Forest Alliance Group (6 alliances), concentrated east of 101°E in southeast Yunnan with limited latitudinal range; (3) Castanopsis–Camellia Forest Alliance Group (2 alliances), restricted to higher-elevation mountainous areas within 103–104° E and 22.5–23° N. Climatic variation accounted for 81.1% of the species compositional variation among alliance groups, with contributions of 83.5%, 57.6%, and 62.1% to alliance-level differentiation within alliance groups 1, 2, and 3, respectively. Precipitation days in the driest quarter (PDDQ) and precipitation seasonality (PS) emerged as the strongest predictors of community differentiation at both alliance group and alliance levels. Topography and soil features significantly influenced alliance differentiation in Groups 2 and 3. Collectively, the interaction between the monsoon climate and topography dominate the spatial differentiation of MEBF communities in Yunnan. Full article

Accurate pre-drilling mud weight window (MWW) prediction is crucial for drilling fluid design and wellbore stability in complex geological formations. Traditional physics-based approaches suffer from subjective parameter selection and inadequate handling of multi-mechanism over-pressured formations, while machine learning methods lack physical constraints and interpretability. This study develops a novel physics-guided deep learning framework integrating rock mechanics theory with deep neural networks for enhanced MWW prediction. The framework incorporates three key components: first, a physics-driven layer synthesizing intermediate variables from rock physics calculations to embed domain knowledge while preserving interpretability; second, a geological sequence-matching algorithm enabling precise stratigraphic correlation between offset and target wells, compensating for lateral geological heterogeneity; third, a long short-term memory network capturing sequential drilling characteristics and geological structure continuity. Case study results from 12 wells in northwestern China demonstrate significant improvements over traditional methods: collapse pressure prediction error reduced by 40.96%, pore pressure error decreased by 30.43%, and fracture pressure error diminished by 39.02%. The proposed method successfully captures meter-scale pressure variations undetectable by conventional approaches, providing critical technical support for wellbore design optimization, drilling fluid formulation, and operational safety enhancement in challenging geological environments. Full article

With the widespread adoption of mobile reading across diverse scenarios, understanding environmental impacts on attention has become crucial for reading performance optimization. Building upon this premise, the study examined the impacts of different reading environments on attention during mobile reading, utilizing a mixed-methods approach that combined eye-tracking experiments with semi-structured interviews. Thirty-two college students participated in the study. Quantitative attention metrics, including total fixation duration and fixation count, were collected through eye-tracking, while qualitative data regarding perceived environmental influences were obtained through interviews. The results indicated that the impact of different environments on mobile reading attention varies significantly, as this variation is primarily attributable to environmental complexity and individual interest. Environments characterized by multisensory inputs or dynamic disturbances, such as fluctuating noise and visual motion, were found to induce greater attentional dispersion compared to monotonous, low-variation environments. Notably, more complex potential task-like disturbances (e.g., answering calls, conversations) were found to cause the greatest distraction. Moreover, stimuli aligned with an individual’s interests were more likely to divert attention compared to those that did not. These findings contribute methodological insights for optimizing mobile reading experiences across diverse environmental contexts. Full article

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details. Full article

The accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persistent cloud cover make consistent monitoring challenging. We integrated multi-temporal Sentinel-2 and Landsat-8 imagery from 2017 to 2021 on the Google Earth Engine platform and applied a sample migration strategy to construct multi-year training data. A random forest classifier was used to identify nine major planting patterns at a 10 m resolution. The classification achieved an average overall accuracy of 88.3%, with annual Kappa coefficients ranging from 0.81 to 0.88. A spatial analysis revealed that single rice was the dominant pattern, covering more than 60% of the area. Temporal variations in cropping patterns were categorized into four frequency levels (0, 1, 2, and 3 changes), with more dynamic transitions concentrated in the central-western and northern subregions. A multiscale geographically weighted regression (MGWR) model revealed that economic and production-related factors had strong positive associations with crop planting patterns, while natural factors showed relatively weaker explanatory power. This research presents a scalable method for mapping fine-resolution crop patterns in complex agroecosystems, providing quantitative support for regional land-use optimization and the development of agricultural policies. Full article

The polymerization of norbornene can occur via ring-opening metathesis polymerization (ROMP) or vinyl-addition pathways, each yielding polynorbornene with distinct structures and properties. This study reports on the synthesis and catalytic application of a new class of vanadium(III) and nickel(II) complexes bearing N-heterocyclic carbene ligands, based on the IPr* framework, for the polymerization of norbornene. The vanadium(III) complexes, activated by diethylaluminum chloride and in the presence of ethyl trichloroacetate, showed activity in ROMP. In contrast, the nickel(II) complexes, activated by methylaluminoxane, exhibited catalytic activity toward vinyl-addition polymerization. Characterization by GPC, NMR, and FTIR confirmed the formation of both ring-opening metathesis polymerization and vinyl-type-derived polynorbornenes, with vinyl-type polymers showing significantly higher molecular weights. Structural variations in the N-heterocyclic carbene ligands, particularly the linker length between imidazole donors, were found to strongly influence polymer molecular weight and the morphology of polynorbornenes. Full article

Wi-Fi fingerprinting is a widely adopted technique for indoor localization in location-based services (LBS) due to its cost-effectiveness and ease of deployment using existing infrastructure. However, the performance of these systems often suffers due to missing received signal strength indicator (RSSI) measurements, which can arise from complex indoor structures, device limitations, or user mobility, leading to incomplete and unreliable fingerprint data. To address this critical issue, we propose Feature-level Augmentation for Localization (FALoc), a novel framework that enhances Wi-Fi fingerprinting-based localization through targeted feature-level data augmentation. FALoc uniquely models the observation probabilities of RSSI signals by constructing a bipartite graph between reference points and access points, which is then processed by a variational graph auto-encoder (VGAE). Based on these learned probabilities, FALoc intelligently imputes likely missing RSSI values or removes unreliable ones, effectively enriching the training data. We evaluated FALoc using an MLP (Multi-Layer Perceptron)-based localization model on the UJIIndoorLoc and UTSIndoorLoc datasets. The experimental results demonstrate that FALoc significantly improves localization accuracy, achieving mean localization errors of 7.137 m on UJIIndoorLoc and 7.138 m on UTSIndoorLoc, which represent improvements of approximately 12.9% and 8.6% over the respective MLP baselines (8.191 m and 7.808 m), highlighting the efficacy of our approach in handling missing data. Full article

Ship detection in complex environments presents challenges such as sea surface reflections, wave interference, variations in illumination, and a range of target scales. The interaction between symmetric ship structures and wave patterns challenges conventional algorithms, particularly in maritime wireless networks. This study presents YOLO-StarLS (You Only Look Once with Star-topology Lightweight Ship detection), a detection framework leveraging wavelet transforms and multi-scale feature extraction through three core modules. We developed a Wavelet Multi-scale Feature Extraction Network (WMFEN) utilizing adaptive Haar wavelet decomposition with star-topology extraction to preserve multi-frequency information while minimizing detail loss. We introduced a Cross-axis Spatial Attention Refinement module (CSAR), which integrates star structures with cross-axis attention mechanisms to enhance spatial perception. We constructed an Efficient Detail-Preserving Detection head (EDPD) combining differential and shared convolutions to enhance edge detection while reducing computational complexity. Evaluation on the SeaShips dataset demonstrated YOLO-StarLS achieved superior performance for both mAP50 and mAP50–95 metrics, improving by 2.21% and 2.42% over the baseline YOLO11. The approach achieved significant efficiency, with a 36% reduction in the number of parameters to 1.67 M, a 34% decrease in complexity to 4.3 GFLOPs, and an inference speed of 162.0 FPS. Comparative analysis against eight algorithms confirmed the superiority in symmetric target detection. This work enhances real-time ship detection and provides foundations for maritime wireless surveillance networks. Full article

Due to the long operation period of Beijing Metro Line 2 and the complex surrounding building environment, this paper comprehensively studied the mechanical properties of new tunnels using close-fitting undercrossing based on pre-support technology. To control structural deformation caused by the expansion project, methods such as laboratory tests, numerical simulation, and field tests were adopted to systematically analyze the tunnel mechanics during the undercrossing of existing metro lines. First, field tests were carried out on the existing Line 2 and Line 3 tunnels during the construction period. It was found that the close-fitting construction based on pre-support technology caused small deformation displacement in the subway tunnels, with little impact on the smoothness of the existing subway rail surface. The fluctuation range was −1 to 1 mm, ensuring the safety of existing subway operations. Then, a refined finite difference model for the close-fitting undercrossing construction process based on pre-support technology was established, and a series of field and laboratory tests were conducted to obtain calculation parameters. The reliability of the numerical model was verified by comparing the monitored deformation of existing structures with the simulated structural forces and deformations. The influence of construction methods on the settlement changes of existing line tracks, structures, and deformation joints was discussed. The research results show that this construction method effectively controls the settlement deformation of existing lines. The settlement deformation of existing lines is controlled within 1~3 cm. The deformation stress of the existing lines is within the concrete strength range of the existing structure, and the tensile stress is less than 3 MPa. The maximum settlement and maximum tensile stress of the station in the pre-support jacking scheme are −5.27 mm and 2.29 MPa. The construction scheme with pre-support can more significantly control structural deformation, reduce stress variations in existing line structures, and minimize damage to concrete structures. Based on the monitoring data and simulation results, some optimization measures were proposed. Full article

The LLC resonant converter, as an isolated DC-DC conversion topology, has been widely adopted in industrial applications. However, when operating under wide input/output voltage ranges, a broad switching frequency range is required to achieve the desired voltage gain. This wide frequency variation complicates the design of magnetic components, causes loss of soft-switching characteristics, and deteriorates electromagnetic interference (EMI) performance. To address these challenges, this paper presents a detailed analysis of the L-LCLC resonant converter. By controlling the connection/disconnection of additional inductors and capacitors through switching devices, the topology achieves structural reconfiguration to enhance the voltage gain range. Optimal mode transition points are selected to ensure stable operation during mode transitions, thereby reducing design complexity, minimizing transition losses, and suppressing voltage/current stress. The parameter design methodology for the additional reactive components is systematically developed. The converter’s performance is validated with Simulink, and the experimental prototype is established with 100 W. Both simulation and experimental results confirm that the L-LCLC resonant converter achieves a wide voltage gain range within a narrow frequency band while maintaining stable mode transitions. Full article

The full-waveform spaceborne laser altimeter improves footprint geolocation accuracy through waveform matching, providing critical data for on-orbit calibration. However, in areas with significant topographic variations or complex surface characteristics, traditional waveform matching methods based on the Pearson correlation coefficient (PCC-Match) are susceptible to errors from laser ranging inaccuracies and discrepancies in surface structures, resulting in reduced footprint geolocation stability. This study proposes a terrain-constrained cross-correlation matching (TC-Match) method. By integrating the terrain characteristics of the laser footprint area with spaceborne altimetry data, a sliding “time-shift” constraint range is constructed. Within this constraint range, an optimal matching search based on waveform structural characteristics is conducted to enhance the robustness and accuracy of footprint geolocation. Using GaoFen-7 (GF-7) satellite laser footprint data, experiments were conducted in regions of Utah and Arizona, USA, for validation. The results show that TC-Match outperforms PCC-Match regarding footprint geolocation accuracy, stability, elevation correction, and systematic bias correction. This study demonstrates that TC-Match significantly improves the geolocation quality of spaceborne laser altimeters under complex terrain conditions, offering good practical engineering adaptability. It provides an effective technical pathway for subsequent on-orbit calibration and precision model optimization of spaceborne laser data. Full article