Search Results (33,678)

Unmanned Aerial Vehicles (UAVs) increasingly operate in missions requiring the simultaneous satisfaction of multiple objectives: reaching task locations, performing the correct service, and preserving sufficient onboard energy for continuous operation. Mission efficiency depends not only on task completion but also on managing the trade-off between service duration and battery recharging. This work proposes a Double Deep Q-Network (DDQN) policy for energy-aware UAV navigation on graph maps. The UAV must first collect the appropriate servicing tool from a depot node and then deliver it to the active failure node. At the same time, it autonomously decides when to interrupt the mission for recharging so as to ensure sufficient battery reserve throughout continuous operations, while minimizing task-servicing duration. The key contribution is an energy-aware reward based on a Dynamic Battery Threshold (DBT) computed from graph shortest-path distances to the nearest charging station, enabling a topology-aware recharge policy that is safer yet less conservative than a per-map tuned safety margin. Extensive Monte Carlo tests on increasingly complex graphs show that the proposed policy achieves a 100% task completion rate with always sufficient final battery to reach a charging node from the task node, while degrading less with map complexity and exhibiting greater robustness to stochastic battery dynamics than a pseudo-optimal baseline. Full article

Remote sensing imagery provides a primary data source for large-scale surface water body monitoring, which is crucial for quantifying climate-related hydrological impacts, supporting flood control, and sustaining integrated water resource management. However, remote sensing images generally face the trade-off between spatial resolution and temporal coverage. To address this issue, the Real-ESRGAN super-resolution algorithm is employed to reconstruct temporally continuous, wide-coverage medium-resolution imagery to a 2.5 m resolution, effectively improving its capability to identify sub-pixel river boundaries. Water body segmentation (WBS) is an effective method for fine-detail surface water extraction. Nonetheless, when applied in complex hydrological environments, it still faces several limitations, such as ambiguous delineation of land–water boundaries and the difficulty in capturing multiscale water body characteristics. To address these issues, a Dynamic Weight Fusion SegFormer (DWFSeg) network is constructed, integrating a MixVision Transformer (MVT) encoder with a multiscale decoding architecture. Specifically, a Dynamic Multiscale Feature Fusion (DMFF) mechanism is proposed, which adaptively assigns semantic-guided fusion weights to multiscale feature water bodies. Furthermore, the Dual Attention-Enhanced (DAE) module strengthens discriminative essential features and suppresses background noise in both channel and spatial dimensions. Evaluated on a self-constructed super-resolution imagery dataset (SID) and the public GID, DWFSeg achieves overall accuracies of 98.08% and 96.14%, respectively. It outperforms representative benchmark models across multiple quantitative metrics, while maintaining competitive inference efficiency and favorable segmentation stability. Ablation studies verify the effectiveness and necessity of each proposed component. The presented network provides a reliable technical solution and supports refined water resource evaluation and sustainable watershed management. Full article

Exact probabilistic inference in Bayesian Networks (BNs) becomes increasingly expensive as network size and structural complexity grow, limiting its applicability in time-sensitive decision-support systems. This study presents a hybrid inference framework that accelerates the deterministic component of Bayesian reasoning by integrating Boolean Satisfiability (SAT) techniques with Bayesian Networks. The proposed approach transforms deterministic conditional probability table (CPT) entries into conjunctive normal form (CNF), enabling SAT-based logical inference over deterministic constraints while preserving the original Bayesian model for probabilistic reasoning. The framework was evaluated on 25 benchmark Bayesian networks using five independent executions per dataset under identical experimental conditions. Performance was assessed through execution time, instrumented operation counts, and inference coverage, with results reported as mean values, standard deviations, and 95% confidence intervals. Experimental results demonstrate substantial reductions in deterministic inference time while maintaining high coverage of deterministic variable assignments across the evaluated benchmarks. Throughout this paper, the reported performance gains refer exclusively to empirical reductions in execution time and instrumented operation counts. They should not be interpreted as evidence of a reduction in the asymptotic computational complexity of exact Bayesian inference, which remains #P-complete in the general case. Rather, the proposed framework provides an efficient mechanism for accelerating deterministic logical inference within Bayesian Networks under the evaluated benchmark conditions. Full article

When UAV(Unmanned Aerial Vehicle) carry out wildlife inspection for biodiversity protection, there are challenges such as low target, complex background, variable shape and serious occlusion, which lead to insufficient accuracy and a high misjudgment rate of the existing lightweight detection model. We propose an improved lightweight YOLOv8n model, which aims to achieve higher accuracy and more real-time animal target detection under the UAV platform. To address the issue of small target features being easily lost in the deep network, we introduce a dynamic upsampling convolution for accurate feature-aware upsampling, which can effectively reconstructs target details and suppress background noise. In order to enhance the feature discrimination ability of the model in complex environments, a convolution block attention mechanism was integrated in the model, and the key features of the target were adaptively focused through the channel–spatial dual attention mechanism. Finally, in order to improve the positioning accuracy in dense and occluded scenes, we used MPDIoU loss function to optimize the bounding box regression, and achieve more stable and accurate alignment by minimizing the vertex distance between the prediction box and the real box. Experiments on public data sets show that the detection accuracy and efficiency of the proposed model are significantly improved compared with the original YOLOv8n: the number of model parameters is reduced by 10.7%, the amount of calculation is reduced by 9.9%, and the inference speed is improved by 25%. In terms of comprehensive performance, our method achieved a mAP@0.5 of 96.4%, a mAP@0.5:0.95 improvement of 6.0 percentage points, and an F1 score of 93.5%, while also significantly reducing the false positive rate. Experiments on self-made aerial animal data sets further fully verify that the algorithm can achieve high-precision real-time animal target detection in the actual UAV platform. Full article

The overall influences of grazing practice on soil microbial community shifts have received considerable attention, but how dung-induced community coalescence affects soil microbial diversity, structure, interaction and the underlying assembly mechanism remains unclear. To address this, we investigated soil microbial community alterations in response to dung deposition, which included three different dung sources in a single grassland. The results indicated that dung deposition by different livestock species had varying impacts on soil microbial community diversity and structure, with cattle dung associated with the largest observed shifts in soil microbial diversity and community structure in this study. The structure of the soil microbial community was strongly associated with multiple edaphic properties (e.g., pH and nutrient content), but these correlations were reshaped by dung deposition in a dung-source-dependent manner. In addition, dung deposition consistently reduced the complexity and robustness of the co-occurrence network across different dung sources, and the strongest alterations in the network were found in shallow soils (0–20 cm). Null model analysis suggests that dung deposition improved the proportional contribution of the stochastic process in bacterial and fungal communities and conversely increased the deterministic process in the archaeal community, implying distinct assembly mechanisms of those microbial domains to the disturbance induced by dung deposition. These results highlight the source effect of dung deposition on the diversity and structure of soil microbial communities, as well as the domain-dependence of dung deposition on the assembly mechanism. The findings suggest that multispecies grazing is associated with distinct dung-induced microbial community shifts, highlighting the need for future research that explicitly incorporates dung source as a variable in grassland soil microbial assessments. Full article

Missing values in time series are not uncommon due to system failures or external interference during data collection. A multitude of imputation algorithms have been proposed to infer these missing values. However, existing methods often overlook the difference in sample data quality within the dataset. Specifically, training imputation algorithms on low-quality samples can lead to the generation of poor-quality data, which adversely affects the performance of downstream models. To address this issue, we propose integrating a data quality evaluation with the imputation process. The workflow involves imputing missing values using an imputation algorithm, evaluating the data quality of each sample, and then removing low-quality samples. Our experimental results demonstrate the effectiveness of this approach on improving the performance of downstream model across four datasets, seven input algorithms, four quality assessment methods, and two types of machine learning tasks. Additionally, we convert time series data into complex networks and find that network features can effectively explain the data quality of individual samples. Full article

This study investigates the nanomechanical response of aluminum substrates coated with crystalline or amorphous equiatomic FeNiCrCo compositionally complex alloy (CCA) layers using molecular dynamics nanoindentation. We evaluated the influence of coating microstructure and pre-relaxation via Monte Carlo/molecular dynamics (MC/MD) on deformation behavior at shallow (35 Å) and deep (65 Å) indentation depths. The relaxation process is critical for equilibrating internal stresses and homogenizing the initial stress field in amorphous phases, while preventing chaotic defect multiplication in crystalline lattices, yet it simultaneously promotes Fe and Cr surface segregation consistent with the equilibrium chemical short-range ordering of the alloy. The results reveal distinct deformation mechanisms: crystalline coatings exhibit higher peak indentation forces of about 300 ± 16 eV/Å characterized by discrete force fluctuations indicative of localized plastic events, while amorphous coatings show lower peak loads (~170–220 ± 12 eV/Å), corresponding to a reduction in load-bearing capacity of roughly 25%–40%, and smooth, continuous deformation governed by shear transformation zones. Notably, in amorphous systems, pressure-induced local crystallization occurs under load, with ordered FCC/HCP regions persisting after unloading, indicating partial irreversibility of the phase transition. Upon deep indentation into the substrate, the amorphous system exhibits a sharp increase in stiffness due to substrate compaction, whereas the crystalline system maintains high load-bearing capacity with reduced defect density in the relaxed state compared to the non-relaxed counterpart. Relaxation significantly reduces force-curve fluctuations in both systems, enhancing the stability of the mechanical response. Compared with uncoated aluminum, which exhibits extensive twin propagation and deep defect penetration, the FeNiCrCo-coated systems approximately halve the defect penetration depth and reduce the defective-atom volume fraction in the substrate by about a factor of two, thereby more effectively confining plastic deformation and preserving substrate integrity under the simulated conditions. These findings demonstrate that the synergy between coating crystallinity and rigorous relaxation protocols governs stress distribution patterns—localized hotspots in amorphous phases versus extended networks in crystalline ones—providing key insights for designing advanced protective coating–substrate systems with optimized mechanical performance. Full article

Fine-grained monitoring of aquatic vegetation in crab ponds is essential for regulating water quality, sustaining ecological balance, and optimizing Chinese mitten crab (Eriocheir sinensis) aquaculture. However, owing to the complex water environment, fragmented vegetation morphology, and the absence of dedicated annotated datasets, traditional remote sensing techniques struggle to achieve highly accurate semantic segmentation and classification. In this study, we construct the first unmanned aerial vehicle (UAV) multispectral dataset for crab pond aquatic vegetation, encompassing four species, Alternanthera philoxeroides, Vallisneria natans, Hydrilla verticillata, and Elodea nuttallii, with pixel-level annotations verified by field surveys across typical aquaculture sites in Jiangsu Province, China. Furthermore, we introduce the Multi-scale Frequency–Spatial Collaborative Network (MFSCNet), built upon a MedNeXt backbone and augmented with distributed modules, including Channel Reduction Attention, Spatial Frequency Selection, a spatial–frequency fusion module, and Mobile Graph Convolution that operate cooperatively across the encoder, skip connections, decoder, and output head. This design suppresses complex water-background interference, enhances vegetation texture representation, and preserves the spatial continuity of vegetation patches. Experimental results demonstrate that, with a lightweight parameter size of merely 19.38 M, MFSCNet achieves a remarkable mean Intersection over Union (mIoU) of 0.9044, outperforming various mainstream convolutional neural network (CNN) and Transformer-based architectures. This study not only provides a high-precision remote sensing technical framework for the accurate multi-class identification and quantitative assessment of aquatic vegetation in crab ponds but also establishes reliable data support for refined aquaculture management and aquatic ecological conservation. Full article

Infrared small target detection (ISTD) remains challenging in complex cluttered environments because targets usually occupy only a few pixels and exhibit weak thermal radiation with limited texture information. The problem becomes more severe in high-resolution infrared imaging systems, where sliding-window inference is commonly adopted under memory and computational constraints. However, the truncated field of view may lead to contextual information loss and increased false alarms in cluttered regions. To address these issues, we propose the Selective Contextual Uplift and Attention Network (SCUA-Net). The proposed network adopts a U-Net++-style densely nested encoder–decoder architecture to enhance multi-scale feature interaction and preserve fine-grained weak-target features. In addition, a Global-Context Calibration Coordinate Attention (GCC-CA) module is introduced to inject window-level contextual statistics into coordinate attention, thereby improving clutter suppression and localization robustness under sliding-window inference. During training, a joint optimization strategy combining Online Hard Example Mining (OHEM) and Dice Loss is employed to alleviate severe foreground–background imbalance. During inference, Gaussian-weighted fusion is adopted to reduce stitching artifacts between adjacent windows. Experimental results on NUDT-SIRST and IRSTD-1k validate the effectiveness of the proposed method. SCUA-Net achieves 99.15% Pd, 0.558 × 10⁻⁶ Fa, and 0.9570 IoU on NUDT-SIRST, while maintaining competitive performance on IRSTD-1k at 161.6 FPS on an NVIDIA RTX 4090 platform, demonstrating favorable accuracy, robustness, and real-time performance in complex infrared scenarios. Full article

The Yili River Valley in Xinjiang is characterized by complex geological structures and frequent geological hazards, which seriously threaten local lives, property, and infrastructure. Improving the accuracy and interpretability of geological hazard assessment is therefore of great significance. To address this, nine factors, including elevation, distance from fault, and slope, were selected to construct a WOE-BPNN-SHAP coupled model. The weights of evidence (WOE) method was first used for factor correlation testing and to optimize the input of the BP neural network. The evaluation accuracies of WOE, WOE-DNN, and WOE-BP models were then compared, and the SHAP model was introduced to analyze the coupling relationships among factors. Results show that the WOE-BP model achieves the best predictive performance, with an AUC of 83.65%. Areas of extremely high-risk account for 8.63% of the study area, while higher-risk areas account for 15.39%. Elevation (1688–2847 m), distance from fault (<3000 m), precipitation (192.6–290.8 mm), and slope (>16°) are identified as the main driving factors. This coupled method provides a new technical approach for regional geological hazard assessment and offers a theoretical basis for disaster prevention, mitigation, and resilience building in the Yili River Valley. Full article

High-altitude ecosystems are characterized by extreme environmental conditions that strongly influence microbial community structure and function. However, whether soil and water microbiomes exhibit similar ecological responses to environmental variation in alpine meadow ecosystems on the Qinghai–Tibetan Plateau remains poorly understood. Here, we combined 16S rRNA gene amplicon sequencing and metagenomic sequencing to compare soil and water microbiomes across two regions (LZ and NQ) with distinct physicochemical profiles. Environmental heterogeneity was more pronounced in water habitats, where all measured parameters (pH, total nitrogen, total organic carbon, and chemical oxygen demand) varied significantly between sites (p < 0.001). Correspondingly, water microbiomes exhibited greater regional differentiation than soil microbiomes, evidenced by stronger beta-diversity separation (PERMANOVA, R² = 0.667 vs. 0.376) and a lower proportion of shared ASVs (65.3% vs. 97.2%). Ecological assembly analysis revealed a sharp contrast: water communities were primarily governed by deterministic processes (accounting for >80% of assembly, with heterogeneous selection as the dominant driver), whereas soil microbiomes were dominated by stochastic processes (>50%). Furthermore, water microbiomes underwent more intense network restructuring, with interaction complexity increasing significantly from 70 nodes and 268 edges in the LZ region to 130 nodes and 577 edges in the NQ region, whereas soil networks remained relatively stable (146 nodes/368 edges to 128 nodes/391 edges). Functional profiling further indicated broader regional redistribution in water compared to the relatively conserved functional framework of soil communities. Resistome analysis identified distinct ARG structures between habitats while revealing 25 overlapping categories, suggesting potential ecological connectivity. Collectively, our findings demonstrate that water microbiomes are more sensitive to regional environmental variation than soil microbiomes, with aquatic communities responding through deterministic restructuring and heightened interaction complexity. These results provide quantitative evidence that high-altitude soil and water microbiomes adopt distinct ecological strategies, offering new insights into the mechanisms governing microbial adaptation and antibiotic resistance distribution. Full article

Few-shot remote sensing scene classification aims to accurately recognize unseen scene categories using only a scarce number of labeled samples, which has emerged as a research hotspot in the field of remote sensing image interpretation. However, remote sensing images intrinsically suffer from large intra-class variations, high inter-class similarities, and complex background interferences. Traditional few-shot learning methods typically perform feature metric learning in Euclidean space, making it difficult to capture the non-Euclidean geometric distribution characteristics of remote sensing features, and they often neglect the spatial structural information embedded in feature maps. To address these issues, this paper proposes a novel few-shot remote sensing scene classification method, termed ZSFS-RGBN, which integrates a Zigzag Scanning Feature Sequence with a Riemannian Geometric Barycenter Network. Specifically, ResNet12 is first employed as the backbone to extract deep convolutional feature maps from both the support and query sets. Second, a Zigzag scanning strategy is introduced to reorganize the two-dimensional feature maps into one-dimensional feature sequences, thereby effectively preserving the spatial locality and structural continuity of the features. Third, an autoregressive moving average (ARMA) model is constructed to characterize the spatial dependencies of the feature sequences, and its state parameters are mapped onto a symmetric positive definite (SPD) matrix manifold, enabling the deep semantic representations of remote sensing scenes in a non-Euclidean geometric space. Finally, a Riemannian geometric barycenter network is designed to learn the Riemannian barycenter of each category on the SPD manifold, where a joint loss function is introduced to simultaneously optimize intra-class compactness and inter-class separability. Comprehensive experiments are conducted on three public remote sensing scene datasets: NWPU-RESISC45, UC Merced Land-Use, and WHU-RS19. Experimental results demonstrate that the proposed method consistently outperforms several representative state-of-the-art approaches under both 5-way 1-shot and 5-way 5-shot settings. Furthermore, ablation studies verify the effectiveness of each component within the proposed framework. Full article

The global maritime sector is undergoing rapid transformation, creating an urgent need to align digital port technologies with a sustainable development framework. However, existing research on smart ports and the blue economy is fragmented and predominantly driven by deterministic approaches that overlook systemic complexity and uncertainty. This study develops a smart port system model grounded in blue economy principles, using a Bayesian network to analyze causal relationships among operational, environmental, and governance variables under uncertainty. The model incorporates key factors including port operational efficiency, logistics reliability, environmental compliance systems, coastal employment, and regulatory enforcement. The findings indicate that operational and logistical factors are the primary drivers of the system, while environmental and socioeconomic variables strongly shape sustainability outcomes. Scenario analysis shows that coordinated interventions targeting these key variables generate the greatest improvements in Smart Port–Blue Economy integration. Sensitivity analysis further identifies coastal economic output, regional competitiveness, and marine ecosystem health as the most responsive outcome variables. The research offers lessons for policymakers to enhance port management by integrating logistics and technological considerations with blue economy principles to design adaptive and resilient policies, particularly in island regions. Full article

Learning-based multi-view 3D reconstruction pipelines, such as transformer-based approaches, enable the accurate reconstruction of 3D scenes from multiple images, but their deployment across the edge–cloud continuum is challenging due to high computational demands and large intermediate data transfers. Effective pipeline scheduling in the continuum must therefore balance latency constraints with the cost of cloud resource usage. In this work, we address cost-aware scheduling under latency constraints for a multi-stage 3D reconstruction pipeline consisting of depth estimation, transformer-based multi-view fusion, and point cloud merging with export to a rendering-ready representation. We implement a service-oriented pipeline where each stage can be executed either on edge or cloud nodes, and we experimentally characterize its performance on representative hardware platforms. The results show a strong imbalance between the computational time and communication latency across platforms, mainly due to large intermediate data. Based on these insights, we propose an online scheduler that dynamically selects stage placements to minimize the cloud cost while satisfying latency constraints. The scheduler incorporates a top-K edge selection mechanism that reduces the decision complexity by jointly considering the network conditions and node utilization. Simulation results parameterized with real-system measurements show that the proposed approach effectively reduces cloud usage while meeting latency constraints, outperforming the baseline strategies based on single-node pipeline execution. Full article

Aiming at the problems of the traditional Statistical Priority-Based Multiple Access (SPMA) protocol in multi-hop Flying Ad Hoc Networks (FANETs), such as single-dimensional queueing only according to priority, unreasonable First-In-First-Out (FIFO) scheduling, high timeout dropping probability of multi-hop forwarding packets, and insufficient utilization of channel opportunities, this paper proposes a multi-hop adaptive SPMA protocol (MA-SPMA) suitable for dynamic multi-hop scenarios. The protocol adopts the Neighbor-Priority Two-Dimensional Queueing (NPTQ) mechanism to store packets jointly according to the next-hop neighbor and priority. A Priority-Utility Dual-round Decision (PUDD) mechanism is designed: in the first round, candidate queues that meet channel load conditions are selected in parallel; in the second round, a utility function constructed by normalized delay, priority, and the end-to-end transmission success rate is used to select the optimal packet for transmission. Theoretical analysis shows that the time and space complexity of MA-SPMA are linearly related to the number of neighbor nodes, with controllable overhead, which is suitable for resource-constrained Unmanned Aerial Vehicle (UAV) platforms. In the MATLAB simulation environment, the Reference Point Group Mobility (RPGM) model is used to construct a multi-hop topology, and comparisons are conducted with two typical improved protocols for multi-hop networks: DCLS-SPMA and BiLSTM-SPMA. The results show that the proposed protocol can significantly improve the end-to-end transmission success rate and network throughput, with more obvious advantages in scenarios with a high proportion of multi-hop services. This paper provides an effective solution for Medium Access Control (MAC) protocol design in FANETs. Full article