Search Results (999)

Ducted fan flying robots with robotic arms can perform physical interaction tasks in complex environments such as indoors. However, the coupling effects between the aerial platform, the robotic arm, and physical environment pose significant challenges for the robot to accurately approach and stably contact the target. To address this problem, we propose a unified control framework for a ducted fan flying robot that encompasses both flight planning and physical interaction. This contribution mainly includes the following: (1) A nonlinear model predictive control (NMPC)-based trajectory optimization controller is proposed, which achieves accurate and smooth tracking of the robot’s end effector by considering the coupling of redundant states and various motion and performance constraints, while avoiding potential singularities and dangers. (2) On this basis, an easy-to-practice hierarchical control framework is proposed, achieving stable and compliant contact of the end effector without controller switching between the flight and interaction processes. The results of experimental tests show that the proposed method exhibits accurate position tracking of the end effector without overshoot, while the maximum fluctuation is reduced by up to 75.5% without wind and 71.0% with wind compared to the closed-loop inverse kinematics (CLIK) method, and it can also ensure continuous stable contact of the end effector with the vertical wall target. Full article

The present study investigated the effects of hierarchical molecular weights and iron chelation on the in vivo absorption and the inflammatory bioactivity of chondroitin sulfate (CS). Firstly, CS, chondroitin sulfate-iron complex (CS-Fe), and low-molecular-weight chondroitin sulfate-iron complex (LCS-Fe) were fluorescently labeled and characterized. Then, the plasma concentration–time profiles and fluorescence imaging results demonstrated that LCS-Fe was more efficiently absorbed into the bloodstream and showed a higher Cmax (415.16 ± 109.50 μg/mL) than CS-Fe (376.60 ± 214.10 μg/mL) and CS (135.27 ± 236.82 μg/mL), and it clearly accumulated in the liver. Furthermore, the anti-inflammatory effect of CS-Fe and LCS-Fe was assayed in LPS-induced macrophages, and LCS-Fe and CS-Fe both showed a better inhibitory effect on NO production, COX-2 and IL-1β gene expression levels compared to CS. Additionally, targeted metabolic analysis of macrophages using LC-MS/MS revealed that CS, CS-Fe, and LCS-Fe could reverse approximately one quarter of the LPS-induced differential metabolites, and the biosynthesis of valine, leucine, and isoleucine was the most significantly involved metabolic pathway. Notably, the molecular weight reduction and iron chelation could both enhance the bioavailability and anti-inflammatory efficacy of CS. Full article

Border surveillance, as a critical component of national security, places increasingly stringent demands on the target perception capabilities of video monitoring systems, especially in wide-area and complex environments. To address the limitations of existing systems in low-confidence target detection and multi-camera collaboration, this paper proposes a novel visual enhancement method for cooperative control of multiple PTZ (Pan–Tilt–Zoom) cameras based on hierarchical reinforcement learning. The proposed approach establishes a hierarchical framework composed of a Global Planner Agent (GPA) and multiple Local Executor Agents (LEAs). The GPA is responsible for global target assignment, while the LEAs perform fine-grained visual enhancement operations based on the assigned targets. To effectively model the spatial relationships among multiple targets and the perceptual topology of the cameras, a graph-based joint state space is constructed. Furthermore, a graph neural network is employed to extract high-level features, enabling efficient information sharing and collaborative decision-making among cameras. Experimental results in simulation environments demonstrate the superiority of the proposed method in terms of target coverage and visual enhancement performance. Hardware experiments further validate the feasibility and robustness of the approach in real-world scenarios. This study provides an effective solution for multi-camera cooperative surveillance in complex environments. Full article

Vessel arrival priority determination is a critical factor affecting port safety and efficiency in maritime traffic management, yet existing approaches relying on First Come, First Served (FCFS) principles or empirical judgment have limitations in systematic decision-making. This study aims to develop a systematic decision-making framework that overcomes these limitations by creating an automated, expert knowledge-based priority determination system for vessel traffic services. A dynamic score-based vessel arrival priority determination model was developed integrating the Delphi technique and Fuzzy Analytic Hierarchy Process (Fuzzy AHP). Basic score evaluation factors were derived through Delphi surveys conducted with 50 field experts, and weights were calculated by differentially applying Fuzzy AHP and conventional AHP according to hierarchical complexity. The proposed model consists of a dynamic scoring system integrating basic scores reflecting vessel characteristics and operational conditions, special situation scores considering emergency situations, and risk scores quantifying safety intervals between vessels. To validate the model performance, simulation-based evaluation with eight scenarios was conducted targeting experienced VTS (Vessel Traffic Services) officers, demonstrating strong agreement with expert judgment across diverse operational conditions. The developed algorithm processes real-time maritime traffic data to dynamically calculate priorities, providing port managers and maritime authorities with an automated decision support tool that enhances VTS management and coastal traffic operations. Full article

Pansharpening fuses multispectral (MS) and panchromatic (PAN) remote sensing images to generate outputs with high spatial resolution and spectral fidelity. Nevertheless, conventional methods relying primarily on convolutional neural networks or unimodal fusion strategies frequently fail to bridge the sensor modality gap between MS and PAN data. Consequently, spectral distortion and spatial degradation often occur, limiting high-precision downstream applications. To address these issues, this work proposes a Hybrid Spectral–Structural Transformer Network (HSSTN) that enhances multi-level collaboration through comprehensive modelling of spectral–structural feature complementarity. Specifically, the HSSTN implements a three-tier fusion framework. First, an asymmetric dual-stream feature extractor employs a residual block with channel attention (RBCA) in the MS branch to strengthen spectral representation, while a Transformer architecture in the PAN branch extracts high-frequency spatial details, thereby reducing modality discrepancy at the input stage. Subsequently, a target-driven hierarchical fusion network utilises progressive crossmodal attention across scales, ranging from local textures to multi-scale structures, to enable efficient spectral–structural aggregation. Finally, a novel collaborative optimisation loss function preserves spectral integrity while enhancing structural details. Comprehensive experiments conducted on QuickBird, GaoFen-2, and WorldView-3 datasets demonstrate that HSSTN outperforms existing methods in both quantitative metrics and visual quality. Consequently, the resulting images exhibit sharper details and fewer spectral artefacts, showcasing significant advantages in high-fidelity remote sensing image fusion. Full article

This study investigates urban–remote disparities in the science performance of Taiwanese eighth-grade students, particularly in matter-related domains, using an explanatory–sequential mixed-methods design. For the quantitative phase, we applied differential item functioning (DIF) analysis with Mantel–Haenszel statistics and logistic regression to the TIMSS 2019 science assessment, while in the qualitative phase, we employed think-aloud interviews and the repertory grid technique (RGT) with 12 students (6 urban, 6 remote) to explore cognitive structures. The quantitative phase identified 26 items (12.3% of 211) disadvantaging remote students, with DIF most pronounced in constructed-response formats and matter-related domains: “Composition of Matter”, “Physical States and Changes in Matter”, and “Properties of Matter”. The follow-up qualitative analyses revealed fragmented, associative cognitive structures in remote learners, marked by reliance on observable (macroscopic) properties rather than microscopic explanations, terminological confusion, microscopic gaps, and misconceptions, contrasting with urban students’ hierarchical integration. Triangulation suggests that the observed disparities are linked to experiential constraints, potentially accounted for by hindered micro–macro connections. Our findings suggest that resource inequities may play a role in sustaining certain biases, indicating that targeted measures could help to make science education more inclusive. Based on these results, we tentatively outline possible educational interventions to improve equity in science education. Full article

As unique geographical entities, island cities boast abundant ecological resources and profound cultural values, serving as critical hubs for maintaining ecosystem services in coastal transition zones. Ensuring the stability of ecosystem services is strategically significant for sustainable urban development, while the construction of Ecosystem Service Networks (ESNs) has emerged as a core strategy to enhance ecological functionality and mitigate systemic risks. Based on current research gaps, this study focuses on three key questions: (1) How to construct a Composite Ecosystem Service Index (CESI) for island cities? (2) How to identify the Ecosystem Service Networks (ESNs) of island-type cities? (3) How to optimize the ecosystem service networks of island cities? This study selects Pingtan Island as a representative case, innovatively integrating the InVEST and MaxEnt models to conduct a comprehensive assessment of ecological and cultural services. By employing Principal Component Analysis (PCA), a Composite Ecosystem Service Index (CESI) was established. The research follows a systematic technical approach to construct and optimize the ESN: landscape connectivity indices were applied to identify ecological source areas based on CESI outcomes; multidimensional resistance factors were integrated into the Minimum Cumulative Resistance (MCR) model to develop the foundational ecological network; gradient buffer zone analysis and circuit theory were sequentially employed to refine the network structure and evaluate ecological efficacy. Key findings reveal: (1) Landscape connectivity analysis scientifically delineated 20 ecologically valuable source areas; (2) The coupled MCR model and circuit theory established a hierarchical ESN comprising 45 corridors (12 Level-1, 14 Level-2, and 19 Level-3), identifying 5.75 km² of ecological pinch points, 7.17 km² of ecological barriers, and 84 critical nodes—primarily concentrated in cultivated areas; (3) Buffer zone gradient analysis confirmed 30 m as the optimal corridor width for multi-scale planning; (4) Circuit theory optimization significantly enhanced network current density (1.653→8.224), demonstrating a leapfrog improvement in ecological service efficiency. The proposed “assessment–construction–optimization” integrated methodology establishes an innovative paradigm for deep integration of ecosystem services with urban spatial planning. These findings provide practical spatial guidance for island city planning, supporting corridor design, conservation prioritization, and targeted restoration, thereby enhancing ecosystem service efficiency, biodiversity protection, and resilience against coastal ecosystem fragmentation. Full article

In uncertain battlefield environments, rapid and accurate detection, identification of hostile targets, and assessment of threat levels are crucial for supporting effective decision-making. Despite offering the advantage of structural transparency, traditional analytical methods rely on expert knowledge to construct models and often fail to comprehensively capture the non-linear causal relationships among complex threat factors. In contrast, data-driven methods excel at uncovering patterns in data but suffer from limited interpretability due to their black-box nature. Owing to probabilistic graphical modeling capabilities, Bayesian networks possess unique advantages in threat assessment. However, existing models are either constrained by the limitation of expert experience or suffer from excessively high complexity due to structure learning algorithms, making it difficult to meet the stringent real-time requirements of uncertain battlefield environments. To address these issues, this paper proposes a new method, the Tree–Hillclimb Search method—an efficient and interpretable threat assessment method specifically designed for uncertain battlefield environments. The core of the method is a structure learning algorithm constrained by expert knowledge—the initial network structure constructed from expert knowledge serves as a constraint, enabling the discovery of hidden causal dependencies among variables through structure learning. The model is then refined under these expert knowledge constraints and can effectively balance accuracy and complexity. Sensitivity analysis further validates the consistency between the model structure and the influence degree of threat factors, providing a theoretical basis for formulating hierarchical threat assessment strategies under resource-constrained conditions, which can effectively optimize sensor resource allocation. The Tree–Hillclimb Search method features (1) enhanced interpretability; (2) high predictive accuracy; (3) high efficiency and real-time performance; (4) actual impact on battlefield decision-making; and (5) good generality and broad applicability. Full article

Coastal ecosystems, located at the interface of terrestrial and marine environments, provide significant ecological functions and resource value. Coastal salt pans, as critical coastal resources with significant implications for coastal ecosystem health and resource management, have attracted extensive research attention. However, current studies on the extraction of spatiotemporal patterns of coastal salt pans remain relatively limited and superficial. This study takes coastal salt pans in Laizhou Bay as a case study, proposing a hierarchical classification method—Salt Pan Feature-Enhanced Fusion Image Random Forest (SPFEFI-RF)—based on multi-index synergy guidance and deep-shallow feature fusion, achieving high-precision extraction of coastal salt pans. First, a Modified Water Index (MWI) and Salt Pan Crystallization Index (SCI) were constructed from image spectral features, specifically targeting the extraction of evaporation ponds. Concurrently, a salt pan sample dataset was developed for the DeepLabv3+ (DL) method to extract deep semantic features and perform multi-scale feature fusion. Subsequently, a three-channel fusion strategy—R(MWI)-G(SCI)-B(DL)—was employed to produce the Salt Pan Feature-Enhanced Fusion Image (SPFEFI), enhancing distinctions between salt pans and background land cover. Finally, the Random Forest (RF) classifier using shallow spectral features was applied to extract salt pan information, further optimized by spatial domain denoising techniques. Results indicate that the SPFEFI-RF approach effectively extracts coastal salt pan features, achieving an overall accuracy of 92.29% and a spatial consistency of 85.14% with ground-truth data. The SPFEFI-RF method provides advanced technical support for high-precision extraction of global coastal salt pan spatiotemporal characteristics, optimizing coastal zone management decisions and promoting the sustainable development of coastal ecosystems and resources. Full article

An accurate perception of upper-level wind fields is essential for improving civil aviation safety and route optimization. However, the sparsity of observational data and the structural complexity of wind fields make reconstruction highly challenging. To address this, we propose QuadMamba-WindNet (QMW-Net), a structure-enhanced deep neural network that integrates a hierarchical state-space modeling framework with a learnable quad-tree-based regional partitioning mechanism, enabling multi-scale adaptive encoding and efficient dynamic modeling. The model is trained end-to-end on ERA5 reanalysis data and validated with simulated flight trajectory observation masks, allowing the reconstruction of complete horizontal wind fields at target altitude levels. Experimental results show that QMW-Net achieves a mean absolute error (MAE) of 1.62 m/s and a mean relative error (MRE) of 6.68% for wind speed reconstruction at 300 hPa, with a mean directional error of 4.85° and an $R^2$ of 0.93, demonstrating high accuracy and stable error convergence. Compared with Physics-Informed Neural Networks (PINNs) and Gaussian Process Regression (GPR), QMW-Net delivers superior predictive performance and generalization across multiple test sets. The proposed model provides refined wind field support for civil aviation forecasting and trajectory planning, and shows potential for broader applications in high-dynamic flight environments and atmospheric sensing. Full article

Synthetic Aperture Radar (SAR) incremental target detection faces challenges from the limits of incremental learning frameworks and distinctive properties of SAR imagery. The limited spatial representation of targets, combined with strong background interference and fluctuating scattering characteristics, leads to unstable feature learning when new classes are introduced. These factors exacerbate representation mismatches between existing and incremental tasks, resulting in significant degradation in detection performance. To address these challenges, we propose a novel incremental learning framework featuring Hierarchical Frequency-Knowledge Reconstruction (HFKR). HFKR leverages wavelet-based frequency decomposition and cross-domain feature reconstruction to enhance consistency between global and detailed features throughout the incremental learning process. Specifically, we analyze the manifestation of representation mismatch in feature space and its impact on detection accuracy, while investigating the correlation between hierarchical semantic features and frequency-domain components. Based on these insights, HFKR is embedded within the feature transfer phase, where frequency-guided decomposition and reconstruction facilitate seamless integration of new and old task features, thereby maintaining model stability across updates. Extensive experiments on two benchmark SAR datasets, MSAR and SARAIRcraft, demonstrate that our method delivers superior performance compared to existing incremental detection approaches. Furthermore, its robustness in multi-step incremental scenarios highlights the potential of HFKR for broader applications in SAR image analysis. Full article

To address the health monitoring and safety assessment challenges of telescopic cranes, this study proposes a comprehensive, online structural performance assessment method based on digital twins, applicable throughout the entire service life cycle of telescopic cranes. The modeling of the telescopic boom and turntable, key components of the target telescopic crane, was carried out using ANSYS Workbench. Working condition sample points were generated through a hierarchical Latin hypercube sampling method, and finite element analysis was conducted to construct a simulation stress database. The fatigue life of the target telescopic crane was analyzed using ANSYS nCode DesignLife to estimate its expected fatigue life. The BayeFsian optimization algorithm was employed to optimize the hyperparameters of BO-LightGBM, which serves as the surrogate model for stress calculations. A digital twin system for the structural performance assessment of telescopic cranes was developed, with a structural performance assessment module at its core. The research findings provide valuable insights for crane structural performance assessments based on digital twin technology. Full article

To enhance the safety risk management and control capabilities for TBM (Tunnel Boring Machine) construction in hydraulic tunnels, this study conducts a correlation analysis and dynamic evolution study of safety risks. Data were collected through multiple channels, including a literature review, on-site records, and expert interviews. Grounded theory was employed for three-level coding to initially identify risk factors, and gray relational analysis was used for indicator optimization, ultimately establishing a safety risk system comprising 5 categories and 21 indicators. A multi-level hierarchical structure of risk correlation was established using fuzzy DEMATEL and ISM, which was then mapped into a Bayesian network (BN). The degree of correlation was quantified based on probabilistic information, leading to the construction of a risk correlation analysis model based on fuzzy DEMATEL–ISM–BN. Furthermore, considering the risk correlations, a safety risk evolution model for TBM construction in hydraulic tunnels was developed based on system dynamics. The validity of the model was verified using the AY project as a case study. The results indicate that the safety risk correlation structure for TBM construction in hydraulic tunnels consists of 7 levels, with the closest correlation found between “inadequate management systems” and “failure to implement safety training and technical disclosure”. As the number of interacting risk factors increases, the trend of risk level evolution also rises, with the interrelations within the management subsystem being the key targets for prevention and control. The most sensitive factors within each subsystem were further identified as adverse geological conditions, improper construction parameter settings, inappropriate equipment selection and configuration, weak safety awareness, and inadequate management systems. The control measures proposed based on these findings can provide a basis for project risk prevention and control. The main limitations of this study are that some probability parameters rely on expert experience, which could be optimized in the future by incorporating more actual monitoring data. Additionally, the applicability of the established model under extreme geological conditions requires further verification. Full article

Background/Objectives: Patients in Intensive Care Units (ICUs) have an elevated risk of infection. Accurate identification of pathogenic bacteria is critical for targeted interventions; however, convolutional neural networks (CNNs) face challenges of high computational demands and parameter redundancy. Methods: We developed a two-stage hierarchical pruning framework for CNN compression (THP-CNN), combining channel importance estimation with receptive field equivalence transformation for a 24-class pathogenic bacteria classification task. Results: THP-CNN (70% pruned) achieves an accuracy of 0.86 with 0.62 M parameters, outperforming ResNet-50 (0.72), MobileNet V2 (0.81), Inception (0.74), and AlexNet (0.62), with the 50% and 60% pruned variants in cross-validation stably maintaining a mean accuracy of 0.79. Conclusions: THP-CNN demonstrates potential for lightweight, real-time bacterial classification, offering a computationally efficient solution for automated pathogen detection. Full article

Automated Vehicles (AVs) are being developed with the aim to reduce the occurrence and severity of Road Traffic Crashes (RTCs). Studies suggest AVs may improve the safety of Vulnerable Road Users (VRUs), particularly on road crossings. However, exposure to novel technology over time may lead to behavioural adaptation. Thus, understanding VRUs’ behavioural intentions towards AVs is crucial for their safe integration into traffic. We investigate four external factors pedestrians consider when crossing a road in front of an AV. An online questionnaire with 281 participants assessed crossing intentions, focusing on road gradient, weather, pedestrian–AV distance, and AV type. Personality traits and self-reported behaviour were measured. Anderson’s experimental protocol revealed all factors significantly influenced crossing decisions. Using hierarchical clustering followed by K-means clustering, the participants were classified into three different profiles: risk-averse, resolute, and indecisive pedestrians. We provide evidence of a strong link between crossing decisions, reported behaviours and psychological facets while interacting with an AV at crossings. Pedestrian profiling allows targeting preventative measures for groups based on unique characteristics, maximising efficiency thereof. Furthermore, pedestrian profiling can inform AV’s driving style to support safer road interactions. This is salient for resolute pedestrians, who take more risks, which may lead to severe RTCs. Full article