Search Results (31,026)

Land subsidence induced by excessive groundwater abstraction has emerged as a major geo-environmental hazard in arid oasis regions, calling for reproducible methods to quantitatively assess the abstraction-reduction–subsidence response and to support zoned management. This study integrates Sentinel-1A PS-InSAR deformation data with groundwater-level measurements to develop and calibrate a MODFLOW-SUB model that couples three-dimensional groundwater flow and one-dimensional skeletal compaction. The InSAR deformation field is used to constrain the conceptual model and key parameters. Four abstraction-reduction scenarios (20%, 40%, 60%, and 80%) are designed to characterize response curves using indicators such as maximum cumulative subsidence, annual subsidence rate, and the area exceeding specified thresholds. In addition, a multi-criteria composite index integrating driving forces, geological susceptibility, and exposure is applied for hazard zoning and scenario comparison. The results show that PS-InSAR constraints improve the spatial agreement of the simulations. The time-series RMSE between simulated and InSAR-derived deformation is approximately 20 mm, and the end-of-period cumulative subsidence error is within 10 mm. From 2019 to 2020, the maximum cumulative subsidence reached 166 mm, and the peak subsidence rate reached 101 mm/a. A clear lag between groundwater-level fluctuations and subsidence is observed, with the maximum correlation occurring at ~35 days for ACJ-1 and ~59–83 days for ACJ-2. This spatial variability is associated with the thickness and permeability of clay layers. Forecasts for 2021–2028 indicate that, under business-as-usual abstraction, maximum subsidence may reach 695 mm. Across scenarios, subsidence mitigation exhibits diminishing marginal returns with increasing abstraction reduction. Under the adopted model settings, a 20% reduction in abstraction yields substantial decreases in maximum subsidence and high-hazard area, representing a practical trade-off between mitigation benefits and water-use costs. Overall, the integrated workflow of monitoring, inversion, coupled modeling, scenario analysis, and zoning, together with the resulting zoned management recommendations, provides decision support for land-subsidence mitigation and water-allocation planning in arid oasis regions. Full article

Urban traffic noise poses escalating environmental challenges in rapidly urbanizing regions with high-density buildings, yet systematic investigations into its spatiotemporal characteristics remain relatively scarce. This study addresses this research gap via the synchronized on-site monitoring of traffic noise and traffic flow on a representative arterial road in Guangzhou, China. The analysis reveals that nighttime equivalent continuous A-weighted sound levels (L_Aeq) are 3.0–4.0 dB(A) higher than those during the congested daytime peak, a phenomenon primarily driven by higher vehicle speeds under nighttime free-flow traffic conditions. The spatial analysis uncovers complex three-dimensional noise propagation dynamics specific to urban street canyons. Vertical profiling demonstrates a counterintuitive pattern where noise levels do not attenuate with building height, and upper floors experience marginally higher noise exposure than the ground floor, which is attributed to the canyon effect, where multiple sound wave reflections offset the natural distance attenuation. A validated three-dimensional computational model was further employed to evaluate the efficacy of noise mitigation strategies, showing that an integrated intervention combining porous asphalt pavement and acoustic barriers achieves a maximum noise attenuation of 19.9 dB(A) at ground-level receptors. This significant reduction stems from a synergistic effect: porous asphalt reduces noise at the source on a global scale, while acoustic barriers provide localized shielding for the lower floors of adjacent buildings. This research concludes that effective traffic noise control in high-density urban areas requires three-dimensional, multi-faceted strategies addressing noise source characteristics, transmission pathways, and receptor vulnerabilities. Full article

Accurate perception of tugboat operational status is essential for optimising port scheduling efficiency and ensuring operational safety. However, existing AIS-based methods often struggle to capture the fine-grained and asymmetric manoeuvring characteristics of tugboats, particularly in distinguishing assisted berthing from unberthing operations. To address these limitations, this study proposes a hybrid recognition framework integrating multidimensional feature engineering with spatiotemporal dynamics. First, a speed-threshold-based sliding window algorithm segments trajectories into sailing and berthing states. Second, a 15-dimensional feature vector—comprising statistical and descriptive features from speed, heading, and trajectory morphology—is constructed to characterise tugboat behaviour. Notably, morpho-logical descriptors such as the ‘Overlap Ratio’ serve as implicit spatial proxies, capturing geographical constraints without reliance on Electronic Navigational Charts. A three-layer fully connected neural network (FCNN) is then developed to classify segments into “Cruising” and “Assisting in Berthing/Unberthing.” Finally, a speed-dynamics rule further distinguishes berthing from unberthing based on opposing temporal evolution patterns. Experiments on real AIS data from Ningbo–Zhoushan Port demonstrate that the model achieves an F1-score of 0.90 and a recall of 0.93 for assistance-related operations. Permutation importance analysis confirms that integrating kinematic and morphological features enables interpretable and precise intent inference. This study offers a high-precision, low-dependency solution for tugboat operation identification, supporting intelligent port surveillance and sustainable maritime management. Full article

Permanent ground deformation caused by fault movement threatens the safe operation of buried pipelines. Accurate fragility assessment of buried pipelines subjected to faulting is essential for pipeline design and risk management. However, buried pipelines exhibit nonlinear mechanical responses due to the coupled effects of multiple factors. Moreover, the effects of key parameters remain insufficiently quantified, limiting the accuracy and engineering applicability of existing fragility assessments. In this study, a three-dimensional finite element model incorporating large deformation and nonlinear pipe–soil interaction is developed and validated against representative experimental data. Using this model, numerical simulations are performed for 352 parameter combinations covering fault type, dip angle, burial depth, soil type, and pipe material. Nonlinear regression of the simulation results yielded predictive models for pipeline peak axial strain under normal-slip and reverse-slip faulting. A fragility framework is then established with fault displacement as the intensity measure, and fragility curves are derived for both faulting modes. The predicted peak axial strains agree with the finite element results: 78.6% (normal-slip) and 72.5% (reverse-slip) of predictions fall within ±20% error. The fragility curves enable quantitative estimation of fault-displacement thresholds. In the case study, the intact-to-damage displacement threshold is approximately 0.6 m for normal-slip faults but approximately 0.2 m for reverse-slip faults, indicating a higher failure likelihood under reverse-slip faulting. Within the investigated parameter ranges, the fault dip angle is the most significant factor affecting the pipeline failure probability for both normal-slip and reverse-slip faulting. Sandy soil and greater burial depth substantially increase the probability of moderate-to-severe damage, whereas higher steel grade increases the displacement threshold for transition from intact to failure. This study provides a rapid quantitative tool and a theoretical basis for pipeline design and risk quantification of buried pipelines in fault zones. Full article

Lonicera caerulea L. (blue honeysuckle) is rich in antioxidant polyphenols, and rapid and accurate determination of its polyphenol content is of great significance for functional food quality control. This study proposed a hybrid variable selection strategy designed for high-dimensional small-sample scenarios and developed a quantitative prediction model for polyphenol content based on mid-infrared (MIR) spectroscopy. A total of 191 Lonicera caerulea samples were collected from Northeast China, and 7468-dimensional spectral data were acquired using a Fourier transform infrared spectrometer. Polyphenol reference values were determined by the Folin–Ciocalteu method. Samples were divided into calibration (n = 152) and prediction (n = 39) sets using the SPXY algorithm. Among the 10 preprocessing methods evaluated, MSC combined with Savitzky–Golay first derivative achieved the best performance and was therefore used for subsequent modeling. The proposed hybrid variable selection method (VIP1.0∩RFR30%) intersected PLS variable importance in projection (VIP ≥ 1.0) with the top 30% important variables from random forest regression, selecting 984 key wavelengths and achieving 86.8% dimensionality reduction. A three-stage hyperparameter tuning strategy was implemented across four models (PLS, RFR, SVR, and XGBoost) to validate feature stability and control overfitting. The optimized XGBoost model achieved excellent performance on the independent test set ($R^2$ = 0.92, RMSE = 0.098, RPD = 3.47). Compared with the classical CARS method ($R^2$ = 0.78, RPD = 2.14), $R^2$ improved by 16.3% and RPD improved by 55.2%. The results demonstrate that the proposed hybrid variable selection strategy can effectively address the challenges of high-dimensional MIR spectral data in small-sample modeling, providing a reliable tool for rapid and non-destructive quantitative analysis of polyphenols in Lonicera caerulea. Full article

Due to the gravitational differentiation effect, the oil–water two-phase flow in the horizontal well exhibits significant asymmetry and inhomogeneity in terms of phase distribution and velocity field. The existing logging techniques are difficult to use to precisely characterize the wellbore flow field under these conditions. To solve this problem, this study, based on the logging data of the Capacitance Array Tool, proposes a three-dimensional visualization method for the water holdup field in the wellbore and applies and evaluates three interpolation algorithms: linear interpolation, cubic spline interpolation, and natural neighbor interpolation. This paper relies on the multiphase flow experimental platform and uses industrial white oil and tap water as fluid media for experiments. It systematically studies the three-dimensional imaging characteristics under different angles, flow rates, and water cuts. The results show that the natural neighbor interpolation algorithm, with its advantage in topological reconstruction, effectively overcomes local mutations in complex flow states. It exhibits superior imaging accuracy and robustness under all operating conditions but has higher computational costs. In contrast, linear interpolation and cubic spline interpolation perform well only in stable flow fields with low-to-moderate flow rates and water holdup. In practical applications, for simple flow states, it is recommended to use computationally efficient linear or cubic spline interpolation methods; for complex flow states or scenarios requiring strict imaging details, the natural neighbor interpolation algorithm should be prioritized. Full article

Green infrastructure (GI) provides essential ecosystem services for urban sustainability in the face of urbanization and climate change, including stormwater management, heat mitigation, and reduction in carbon dioxide (CO₂) concentration levels. Existing studies often focus on single-dimensional ecological effects, lacking a systematic investigation of their synergies and trade-offs. This study developed a coupled framework integrating scenario design, model simulation, and multi-indicator evaluation. Fifty-six scenarios, varying by GI combinations, weather conditions, and total annual runoff control rate (RCR), were applied to a high-density industrial district in Nanjing. The results showed that: (1) GI combinations enhanced comprehensive benefits, with the combination including bioretention (BR), permeable pavement (PP), and green roof (GR) performing most effectively. This was followed by the combination of BR and PP, then by BR and GR, while the use of BR alone provided the lowest effectiveness. (2) PP was a key synergistic component, improving heat mitigation and reducing CO₂ concentration levels through the beneficial effects of rainfall events. (3) Exceeding the optimal RCR threshold for some GI combinations diminished tree space and three-dimensional green volume, shifting synergies into trade-offs. (4) Three-dimensional green volume was positively correlated with reductions in Physiological Equivalent Temperature (PET) and CO₂ concentration, confirming its core role. (5) Rainfall boosted carbon sinks, while a significant cooling enhancement required PP. This study elucidates the water–heat–carbon synergy in small-scale GI, supporting multi-objective optimization in high-density urban renewal. Full article

The objective of this study was to explore the effect of the assembly sequences of wall materials on the structure and properties of Antarctic krill peptide (AKP)-loaded ovalbumin (OVA)–chitosan (CS) nanoparticles (NPs). Two AKP-loaded NPs (CS/OVA-AKP and OVA/CS-AKP) were prepared by changing the sequences of OVA and CS. The results confirmed that CS/OVA-AKP had a smaller particle size (291 nm vs. 320 nm), lower polydispersity index (0.233 vs. 0.282), higher absolute zeta potential (34.4 mV vs. 32.1 mV), and higher encapsulation efficiency (81.6% vs. 75.4%) than OVA/CS-AKP. X-ray diffraction analysis confirmed that AKP was encapsulated in an amorphous state within the NPs. Fourier transform infrared spectroscopy and three-dimensional (3D) fluorescence spectroscopy revealed that electrostatic interactions, hydrogen bonding, and hydrophobic interactions were the primary driving forces for nanoparticle formation, with CS/OVA-AKP demonstrating a stronger OVA fluorescence quenching effect. Compared with OVA/CS-AKP, CS/OVA-AKP exhibited better redispersibility, and CS/OVA-AKP showed greater stability under various environmental factors (thermal treatment, salt concentration, pH, and storage time). During simulated gastrointestinal digestion, CS/OVA-AKP effectively protected AKP from gastric degradation and showed a higher AKP release rate in simulated intestinal fluid (61.1%) than OVA/CS-AKP (53.0%). The release followed the Korsmeyer–Peppas model, with OVA/CS-AKP exhibiting non-Fickian diffusion (n = 0.7500), and CS/OVA-AKP approached Case II transport (n = 0.9889), indicating erosion-controlled release behavior. CS/OVA-AKP also demonstrated higher hypoglycemic activity, with inhibition rates of 41.1%, 37.5%, and 36.1% for α-glucosidase, α-amylase, and DPP-IV, respectively. These findings underscore the important influence of wall-material assembly sequences on the structure and properties of AKP-loaded NPs, offering valuable insights for the development of bioactive peptide delivery systems. Full article

Tsunami-induced scour around coastal embankments and nearshore structures is a primary cause of structural instability and failure. However, the hydrodynamic mechanisms by which coastal vegetation mitigates this scour remain insufficiently understood. This study employs three-dimensional numerical simulations to investigate the influence of rigid and flexible vegetation on overflow-induced scour downstream of embankments and local scour around structures under tsunami-like inundation. The simulations were conducted using Ansys Fluent 2021R2, utilizing the Volume of Fluid (VOF) method to capture the free surface and the RNG $k-\epsilon$ turbulence model within the Reynolds-averaged Navier–Stokes (RANS) framework. Computational geometries were reconstructed from laboratory experiments, and the model’s reliability was validated against measured water surface profiles. The results demonstrated that vegetation significantly alters flow dynamics, velocity distributions, vortex structures, and both the magnitude and patterns of bed shear stress within scour holes. Specifically, in overflow-induced scour, vegetation suppresses scour intensity by inducing backwater effects, enhancing momentum diffusion, attenuating flow impingement on the bed, and reducing peak bed shear stress. Conversely, for local scour around structures, vegetation increases upstream water depth while intensifying downstream wake vortices, leading to scour hole elongation—particularly under dense and tall vegetation. These findings offer novel insights into the hydrodynamics of vegetation-induced scour mitigation and provide guidelines for optimizing vegetation configurations to enhance the tsunami resilience of coastal infrastructure. Full article

As network environments become increasingly interconnected, ensuring robust cyber-security has become critical, particularly with the growing sophistication of modern cyber threats. Intrusion detection systems (IDSs) play a vital role in identifying and mitigating unauthorized or malicious activities; however, conventional machine learning-based IDSs often rely on handcrafted features and are limited in their ability to detect diverse attack types across disparate network domains. To address these limitations, this paper introduces a novel unified intrusion detection framework that implements “Structural Dualism” to integrate three heterogeneous benchmark datasets (CSE-CIC-IDS2018, NF-BoT-IoT-v2, and IoT-23) into a harmonized, protocol-agnostic representation. The framework employs a shared autoencoder architecture with dataset-specific projection layers to learn a unified latent manifold. This 15-dimensional space captures the underlying semantics of attack patterns (e.g., volumetric vs. signaling) across multiple domains, while dataset-specific decoders preserve reconstruction fidelity through alternating multi-domain training. To identify complex micro-signatures within this manifold, the framework utilizes a synergistic hybrid convolutional neural network–deep neural network (CNN–DNN) classifier, where the CNN extracts spatial latent patterns and the DNN performs global classification across twenty-five distinct classes. Class imbalance is addressed through resampling strategies such as adaptive synthetic sampling (ADASYN) and edited nearest neighbors (ENN). Experimental results demonstrate remarkable performance, achieving 99.76% accuracy for binary classification and 99.54% accuracy for multi-class classification on the merged dataset, with strong generalization confirmed on individual datasets. These findings indicate that the shared autoencoder-based CNN–DNN framework, through its unique feature alignment and spatial extraction capabilities, significantly strengthens intrusion detection across diverse and heterogeneous environments. Full article

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation. Full article

Ground-based three-dimensional motion testing of space manipulators typically relies on active suspension-based gravity compensation systems. The design of such systems faces two fundamental challenges: first, how multiple suspension winch units can precisely track the dynamic trajectories of the corresponding suspension interfaces on the manipulator; and second, how to achieve optimal collision avoidance among the suspension mechanisms themselves during the tracking process. To address these challenges, this paper presents a multi-point suspension system endowed with kinematic redundancy for the trajectory tracking task, thereby ensuring precise tracking of the manipulator’s complex three-dimensional motions. The key innovation of this work lies in formulating the internal collision avoidance constraints as safety distance functions and integrating them into the system states. These are then combined with the trajectory-tracking states to construct a unified state-extended system model that exhibits typical underactuated characteristics. For this model, and under the concurrent influence of external disturbances from both the manipulator’s motion and the proximity to collision boundaries, a dedicated Model Predictive Controller (MPC) is designed. The results demonstrate that the proposed controller can generate an optimal coordinated collision-avoidance motion plan for the suspension winch units while maintaining precise trajectory tracking, thereby effectively solving the coordinated motion-planning problem for such complex underactuated systems. The proposed MPC achieves maximum tracking errors of 0.64 mm (X) and 0.13 mm (Z)—substantially lower than the 1.3 mm and 1.9 mm results listed in the comparative scheme—while delivering optimal collision avoidance, which is only suboptimally realized in the baseline. Full article

In response to issues such as insufficient bias in random sampling, low convergence efficiency, inadequate path search efficiency, and lack of path smoothness encountered by the traditional RRT* algorithm during path planning, an improved algorithm is proposed. First, a dynamic ellipsoidal sampling strategy is introduced, which accelerates the exploration of the path space by adaptively adjusting the sampling region. Additionally, a bidirectional RRT* algorithm is employed, establishing two alternately growing search trees to perform bidirectional search, thereby effectively enhancing the convergence speed of the algorithm. Second, a dynamic goal-biased strategy is adopted, which greedily guides the random tree to grow rapidly toward the goal point, thereby improving planning efficiency. A heuristic search scheme is integrated with the RRT* algorithm to further increase convergence speed. A random sampling expansion strategy is utilized to guide the tree to expand into unexplored regions, avoiding local minima while ensuring global search capability. Local reconnection optimization is applied to reduce the cumulative path cost of new nodes while balancing path length, smoothness, and safety. To reduce the number of iterations, an improved artificial potential field method is incorporated into the growth process of the bidirectional random search trees, providing directional guidance for their expansion. Finally, path pruning techniques are applied to eliminate redundant nodes from the initial path, and a cubic B-spline interpolation algorithm is used to smooth the pruned path, generating a final trajectory with continuous curvature suitable for tracking. Quantitative analysis of simulation experiments in three-dimensional space shows that in both simple and complex environments, compared with the RRT, GB-RRT, BI-RRT, APF-RRT, and BI-APF-RRT* algorithms, the improved RRT* algorithm reduces planning time by approximately 58–90%, decreases the number of path nodes by about 31–91%, and shortens path length by around 8–20%, demonstrating the superiority of the proposed algorithm. Full article

For arch dam seismic safety evaluation, the finite element equivalent stress method has been widely used, and existing studies have realized mature equivalent stress calculation along the foundation surface path. However, from the scientific research perspective, there is a lack of a full dam surface equivalent stress characterization method for arch dams under seismic action; from the engineering practice perspective, the traditional path method cannot fully reflect the overall stress distribution of the dam, leading to insufficient comprehensive safety evaluation. To accurately assess the impact of seismic action on the overall structural safety of arch dams and address the above limitations, this study develops a methodology for calculating equivalent stress across the entire dam surface of arch dams under seismic action. Taking a concrete arch dam as the research object, a seismic wave input method based on viscoelastic artificial boundaries is employed. Three-dimensional finite element analysis of the arch dam is performed using ABAQUS, integrated with Python-based secondary development to extract stress along the integration path of each arch ring layer and calculate sectional internal forces. The equivalent stress of each arch ring layer integration path is then processed using the material mechanics method to obtain the equivalent stress distribution across the entire dam surface. A comparative analysis is conducted between the equivalent stress on the entire dam surface and that along paths on the foundation surface regarding the seismic dynamic response and behavioral patterns of the dam. The results demonstrate that the full dam surface equivalent stress approach not only accurately captures the extreme tensile and compressive stress values in the downstream foundation area but also identifies stress extrema in the upstream dam crest region, thereby achieving comprehensive characterization of the dam stress field under seismic action and enhancing both the efficiency and accuracy of equivalent stress calculations for arch dams. This method provides more comprehensive and reliable data support for seismic design optimization and reinforcement of arch dams. Compared with the traditional foundation surface path method, the proposed method achieves 100% identification of the whole dam surface stress extremum areas, with a maximum relative error of only 1.62% in the overlapping calculation area. Full article

Hooked-head spur dikes are a specialized type of spur dike, where their geometry significantly influences flow diversion, sediment transport, and bank protection. This study establishes a three-dimensional numerical model utilizing the renormalization group (RNG) k-ε turbulence closure and the volume of fluid (VOF) method to explore the effects of hook angle (90°, 120°, and 150°) and hook-length ratio (L/D = 1/2, 1/3, and 1/4) on the flow structure surrounding a hooked-head spur dike. The study comprises nine simulation cases, and the distributions of mainstream velocity and turbulent kinetic energy (TKE) are analyzed. The results demonstrate that a hook angle of 120° yields the greatest increase in the mean dimensionless mainstream velocity (V*), corresponding to enhancements of 4.26% and 9.09% relative to the angles of 90° and 150°, respectively. When the hook angle is fixed at 120°, increasing the hook length enhances the mainstream velocity; specifically, at L/D = 1/2, the mean V* increases by 10.58% and 14.64% compared to at L/D = 1/3 and 1/4, respectively. Meanwhile, the TKE in the downstream recirculation zone decreases as either the hook angle or the hook length increases. At a hook angle of 90°, the mean dimensionless TKE (E*) is 8.80% and 10.65% higher than at 120° and 150°, respectively. For a fixed hook angle of 120°, the mean E* at L/D = 1/2 decreases by 3.46% and 9.35% compared to at L/D = 1/3 and 1/4, respectively. In summary, the appropriate selection of hook angle and hook length can effectively guide flow toward the channel center, increase conveyance capacity, and enhance hydraulic performance for river regulation. Full article