Search Results (28,189)

Distributed optical fiber vibration sensing (DVS) has shown strong potential in perimeter security, pipeline leakage monitoring, transportation safety, and structural health diagnostics owing to its high sensitivity, long-range coverage, and immunity to electromagnetic interference. However, severe cross-scene distribution mismatch is often encountered in real-world deployments: indoor, outdoor, and pipeline environments exhibit markedly different noise patterns and time–frequency characteristics, thereby degrading the generalization ability of models trained in a single scene. To address this challenge, we propose a Progressive Multi-Strategy Domain Alignment (PMSDA) framework for label-disjoint cross-scene vibration recognition. PMSDA uses a compact expansion–compression encoder together with complementary alignment mechanisms—maximum mean discrepancy (MMD), correlation alignment (CORAL), and adversarial domain discrimination—to learn a scene-robust latent space from a labeled indoor source and two unlabeled target domains (outdoor and pipeline) within a single alternating-training model. Because the fine-grained source and target label spaces are disjoint, PMSDA is formulated as a representation-transfer framework rather than a standard label-shared unsupervised domain adaptation method; target-domain recognition is therefore performed through domain-specific prototype clustering in the aligned latent space. On three representative scenes with nine event classes in total, PMSDA achieved 89.5% accuracy, 86.7% macro-F1, and 0.93 AUC for Indoor→Outdoor, and 85.8%, 84.7%, and 0.87, respectively, for Indoor→Pipeline, outperforming traditional feature+SVM/RF pipelines, CNN/ResNet baselines, and representation-transfer baselines adapted from DANN/CDAN/SHOT under the same evaluation protocol. These results indicate that PMSDA is a promising and effective framework for offline cross-scene DVS evaluation under disjoint target event sets. Full article

The effects of dust, copper particles, and iron particles on the high-temperature oxidative degradation behavior of aviation lubricating oil were systematically examined, and the high-temperature catalytic oxidation effects of single-particle and mixed-particle systems on the lubricating oil were further analyzed, respectively. Gas chromatography/mass spectrometry analysis results indicated that significant differences exist in the catalytic oxidation activity of particles toward lubricating oils, with the activity ranking in the descending order of copper particles > iron particles > dust. Notably, following oxidation by both metal and dust particles, the acid value, particle size, and viscosity of the oil sample exhibit a significant synergistic catalytic effect, even exceeding those of the oil sample oxidized by the same amount of metal particles. Specifically, relative to the pristine oil, the oil oxidized with 5 mg of copper particles and 5 mg of dust exhibits respective increases of 213.3%, 316.11%, and 661.43% in the aforementioned properties. This variation is attributed to the physical adsorption and chemical reactions between dust and antioxidants during oxidation, which deplete antioxidants and thereby exacerbate oil oxidation. Furthermore, this study further elucidates the potential synergistic oxidation mechanism induced by metal particles and dust particles. Full article

Sunflower (Helianthus annuus L.) production is severely hindered by continuous cropping obstacles, leading to soil degradation and significant yield declines. This study compared soybean–sunflower (G-H) and maize–sunflower (Z-H) rotations against sunflower monoculture (H-H) to elucidate the mechanisms of soil health restoration associated with crop rotation. Our results demonstrated that Z-H and G-H rotations led to a profound yield increase of 103.19% and 82.35%, respectively, with Z-H improving the 100-grain weight by 52.63%. Soil biological revitalization was evidenced by a 98.29% increase in sucrase activity and a 28.92% rise in alkaline phosphatase activity. Metagenomic analysis revealed that the rotation sequences increased bacterial Chao1 richness by 35.29% and fungal Shannon diversity by 20.17%. Specifically, the rotation treatments proactively recruited beneficial taxa such as Pontibacter (Log₂FC > 3.0) and Panaeolus (Log₂FC = 6.88), while effectively suppressing pathogens such as Ceratobasidiaceae. Co-occurrence network analysis identified a complex bacterial scaffold (199 nodes, 53 modules) that provided greater structural robustness than the fungal network (27 nodes). It is concluded that diversified rotations effectively mitigate continuous cropping obstacles by reactivating nutrient cycling and restructuring the rhizosphere into a stable, modular microbial interactome. This study provides a quantitative framework for utilizing biological strategies to restore soil health in degraded agroecosystems. Full article

Target tracking for uncrewed aerial vehicles (UAVs) demands both low-latency, real-time inference and robust, long-term temporal consistency. Current approaches often face a trade-off between efficiency and stability in practice. This tension is particularly pronounced in resource-limited UAV platforms: computationally heavy architectures can exceed onboard processing capacity and energy budgets, whereas overly lightweight models degrade temporal state fidelity—leading to cumulative drift under challenging conditions such as occlusion, motion blur, rapid scale variation, and cluttered backgrounds. To address this challenge, we propose SPM-Track, a lightweight yet temporally consistent tracking framework grounded in explicit state maintenance. It introduces a dual-loop judgment-calibration architecture comprising three coordinated components: (1) the content-aware state encoder, which employs input-gate modulation, selectively models temporal dynamics to suppress noise propagation into the state; (2) the hierarchical state manager enhances robustness against long-term occlusions and appearance variations by coordinating short-term state updates with a long-term reliable snapshot library via dual-path cooperation; (3) the adaptive feature recalibration module applies joint spatial-channel discriminative weighting before response map generation, effectively enhancing target distinctiveness and mitigating background clutter interference. Experiments on UAV123, DTB70, UAVTrack112, and LaSOT show that SPM-Track outperforms lightweight baselines and remains competitive with several Transformer-based trackers, demonstrating a favorable trade-off between edge-deployable efficiency and long-term robustness in UAV-based tracking. Full article

The use of shotcrete is a critical support technique in ocean engineering structures. However, it often exhibits low chloride and salt erosion resistance under ocean environmental conditions and poor long-term durability. This study employed polypropylene fiber (PF) and basalt fiber (BF) to optimize the shotcrete mix design. Laboratory immersion and salt spray tests simulated chloride ion corrosion environments in the ocean’s underwater and atmospheric zones. The effects of different corrosion mechanisms and varying fiber volume fractions on shotcrete strength and durability were then analyzed. The results indicate that shotcrete demonstrates strong resistance to chloride-induced corrosion in both ocean underwater and atmospheric zones when the volume fractions of PF and BF are 0.2% and 0.1%, respectively. Based on test results from 3D digital microscopy (3D-DM), X-ray diffraction (XRD), and scanning electron microscopy (SEM), the chloride-induced degradation mechanism of hybrid fiber-reinforced shotcrete was analyzed from both mesoscopic and microscopic perspectives. This study offers theoretical support for applying hybrid fiber-reinforced shotcrete in ocean engineering environments. Full article

Remote sensing (RS) object detection faces intrinsic challenges arising from the overhead imaging paradigm and the diversity of climatic conditions. In particular, atmospheric phenomena such as clouds and haze cause severe visual degradation, making reliable object detection difficult. However, most existing detectors are developed under clear-weather conditions, which limits their generalization capability in realistic haze-degraded RS scenarios. To alleviate this issue, an adaptive spectral-aware network for RS object detection under haze interference is proposed, termed RShDet, which is designed to handle both high-altitude RS imagery and low-altitude Unmanned Aerial Vehicle (UAV) scenarios. Firstly, the Object-Centered Dynamic Enhancement (OCDE) module dynamically adjusts the spatial positions of key-value pairs through query-agnostic offsets, enabling the network to emphasize object-relevant regions while suppressing haze-induced background interference. Secondly, the Dynamic Multi-Spectral Perception and Filtering (DSPF) module introduces a multi-spectral attention mechanism that adaptively selects informative frequency components, thereby enhancing discriminative feature representations in hazy environments. Thirdly, the Frequency-Domain Multi-Feature Fusion (FDMF) module employs learnable weights to complementarily integrate amplitude and phase information in the frequency domain, enabling effective cross-task feature interaction between the enhancement and detection branches. Extensive experiments demonstrate that RShDet consistently achieves superior detection performance under hazy conditions across both synthetic and real-world benchmarks. Specifically, it achieves improvements of 2.4% mAP50 on Hazy-DOTA, 1.9% mAP on HazyDet, and 2.33% mAP on the real-world foggy dataset RTTS, surpassing existing state-of-the-art methods. Full article

Synthetic Aperture Radar (SAR) enables all-weather maritime surveillance, yet ship-oriented bounding box (OBB) detection remains challenging in complex scenes. Strong sea clutter and dense harbor scatterers often mask the slender characteristics of ships as well as the weak responses of small ships. Meanwhile, the periodicity of angle parameterization introduces regression discontinuities, and near-symmetric, bright-scatterer-dominated signatures further cause heading ambiguity, undermining the stability of orientation prediction. Moreover, in most detectors, multi-scale feature fusion and angle estimation lack explicit coordination, and rotated-box localization performance is often jointly affected by feature degradation and unstable orientation prediction. To this end, we propose a unified framework that simultaneously strengthens multi-scale representations and stabilizes orientation modeling. Specifically, we design a Gaussian-Guided Stage-Aware Deformable Feature Pyramid Network (GSDFPN) and a Coarse-to-Fine Unit-Circle Resolver (CF-UCR). GSDFPN enhances multi-scale fusion with two plug-in components: (i) a Gaussian-guided High-level Semantic Refinement Module (GHSRM) that suppresses clutter-dominated semantics while strengthening ship-responsive cues, and (ii) a Stage-aware Deformable Fusion Module (SDFM) for low-level features, which disentangles channels into a geometry-preserving spatial stream and a clutter-resistant semantic stream, and couples them via deformable interaction with bidirectional cross-stream gating to better capture the inherent slender characteristics of ships and localize small ships. For orientation, CF-UCR decomposes angle prediction into direction-cluster classification and intra-cluster residual regression on the unit circle, effectively mitigating periodicity-induced discontinuities and stabilizing rotated-box estimation. On SSDD+ and RSDD, our method achieves AP/AP${}_{50}$/AP${}_{75}$ of 0.5390/0.9345/0.4529 and 0.4895/0.9210/0.4712, respectively, while reaching AP${}_{s75}$/AP${}_{m75}$/AP${}_{l75}$ of 0.5614/0.8300/0.8392 and 0.4986/0.8163/0.8934, evidencing strong rotated-box localization across target scales in complex maritime scenes. Full article

Microplastics have become a ubiquitous environmental contaminant in natural waters, raising significant concerns regarding aquatic ecosystem health and potential human exposure. A comprehensive synthesis of current knowledge on microplastic pollution in freshwater and marine systems is presented, focusing on sources, distribution patterns, environmental behavior, and associated risks. In freshwater environments, microplastic inputs are closely linked to human activities and land use, with wastewater treatment plant effluent, urban runoff, and agricultural drainage serving as major pathways. In marine systems, microplastics undergo dynamic transport influenced by particle properties, hydrodynamic conditions, and biological interactions such as biofouling and aggregation, leading to widespread distribution from coastal zones to deep sea sediments. Importantly, the role of the freshwater–estuarine–marine continuum is emphasized, highlighting the coupled processes of transport, retention, and remobilisation that govern the spatiotemporal distribution and ultimate fate of microplastics across interconnected aquatic systems. Toxicological effects on aquatic organisms are further examined, particularly immunotoxicity and neurotoxicity, alongside potential human health risks via ingestion, inhalation, and dermal exposure. Attention is drawn to the discrepancy between experimental exposure conditions and environmentally relevant concentrations, which constrains robust risk assessment. Current mitigation strategies, including source reduction, wastewater treatment upgrades, transport interception, and degradation technologies, are critically evaluated in terms of effectiveness and limitations. A clear distinction is made between apparent removal and actual degradation, with further consideration of the environmental implications associated with sludge retention and degradation byproducts. Finally, key research priorities are identified, including the need for standardized detection methods, improved exposure assessment, development of environmentally benign alternatives, and strengthened policy-driven source control. These insights provide a basis for advancing sustainable management strategies for microplastic pollution in natural waters. Full article

Seismic collapse can cause catastrophic losses, and acceptable annual collapse probability with its CMR target is a core metric in performance-based design. Existing ATC-63-based CMR research mainly addresses non-damped systems and often uses a single lumped dispersion, obscuring damper-reliability contributions and hindering alignment with CECS 392 limits. This study proposes a unified, code-consistent decision framework for acceptable annual collapse probability and CMR that jointly accounts for seismic hazard and damper-related uncertainty. The total collapse dispersion is decomposed as ${\sigma }_{\mathrm{total},\mathrm{damp}}^2={\sigma }_{\mathrm{base}}^2 +{ \sigma }_{\mathrm{damper}}^2$, where ${\sigma }_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}$ represents background dispersion independent of dampers and ${\sigma }_{\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{e}\mathrm{r}}$ captures incremental uncertainty induced by degradation and partial failure. A code-designed viscous-damped RC frame is evaluated under three scenarios (nominal damping, 20% damping-coefficient reduction, and 7% random damper failures). Using the same 14 records and $\mathrm{S}\mathrm{a}\left({\mathrm{T}}_1,5\%\right)$ as the intensity measure, multi-stripe IDA and Probit-based lognormal fragility fitting yield median collapse intensities $\mathrm{S}\mathrm{c}\approx 2.18-2.24 \mathrm{g}$, with only ~2–3% reduction under mild degradation/failure. A random-effects variance decomposition identifies σ_damper ≈ 0, indicating a limited marginal contribution of damper-related uncertainty within the degradation range considered in this study. Closed-form relationships between annual collapse rate, $\mathrm{S}\mathrm{c}$, and ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ are then derived under a power-law hazard model and inverted to generate acceptable-risk intervals and CMR target curves/matrices. Results show that higher design intensity and larger ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ demand substantially higher CMR, highlighting potential risk underestimation when relying solely on nominal CMR. The framework enables explicit identification of damper-related uncertainty from limited collapse data and provides a practical workflow for collapse-prevention design and post-assessment under explicitly defined scenario conditions, with a clear pathway for extension to broader scenario spaces. Full article

The Qilian Mountains serve as a crucial ecological security barrier in western China, and the soil structural stability of alpine meadows directly affects regional ecological security and the sustainable utilization of grasslands. However, current research on grazing mostly relies on short-term artificially controlled experiments, which differ greatly from the pattern of long-term natural grazing. Herein, this study abandoned the artificially controlled grazing method and selected sampling areas with stable grazing regimes for more than a decade. Taking no grazing (CK) as the control, four treatments were established, including light grazing (LG), moderate grazing (MG), heavy grazing (HG) and extreme grazing (EG). The particle size distribution and stability of mechanically stable and water-stable soil aggregates in different soil layers were determined. Combined with environmental and biological factors, the effects of grazing on the structure and stability of soil aggregates were elucidated. The results showed that no grazing improved the mechanical stability of soil aggregates but reduced their water stability. Light and moderate grazing maintained a balanced and resistant soil structure, with the surface soil being more fragile than the subsurface soil. Heavy and extreme grazing led to severe structural degradation, with the subsurface soil being more fragile than the surface soil. Soil aggregate stability was jointly regulated by elevation, soil properties, root biomass, nitrogen forms, mineralization and microbial biomass. In conclusion, from the perspective of soil structural stability and sustainable utilization, light and moderate grazing represent the optimal utilization mode for the alpine meadows of the Qilian Mountains. This mode not only maintains the structural stability of subsurface soil aggregates but also balances biological cementation and physical disturbance, thus avoiding the insufficient water stability under no grazing and the risk of structural fragmentation under heavy or extreme grazing. Environmental and biological factors mediated the divergent responses of mechanical and water stability to different grazing intensities. The findings of this study provide a scientific basis and new insights for the rational grazing management and soil conservation of alpine meadows in the Qilian Mountains. Full article

Reclaimed asphalt pavement (RAP) from polymer-modified asphalt pavements often contains a recovered binder that is stiff and brittle, which reduces workability and increases durability risk. Waste cooking oil (WCO) is a promising circular rejuvenator, but its effectiveness remains inconsistent because oil source and degradation state are often not well controlled, particularly in polymer-modified RAP systems. This study introduced a controlled simulated WCO approach and compared four oil sources (Palm, Soy, Olive, and Rice) as rejuvenators for recovered RAP binder and RAP mixtures. Simulated oils were added at 4% and 8% by mass of recovered RAP binder. The simulated WCOs produced clear dosage-dependent softening of the recovered binder. Penetration increased, while softening point and rotational viscosity decreased, indicating partial restoration of binder mobility and improved workability. At the mixture level, the 4% dosage provided the most balanced performance, improving moisture resistance and reducing Cantabro loss compared with the control mixture. Specifically, tensile strength ratio (TSR) increased from 75% to 80.9–83.7%, while Cantabro loss decreased from 19.8% to 13.2–14.6%, showing better cohesion and resistance to particle loss. However, Hamburg wheel tracking (HWT) results revealed strong oil-source dependence, with Soy showing the lowest rut depth and Olive the highest, indicating that excessive softening can reduce deformation resistance. The results demonstrate that controlled simulated WCO can support practical oil-source selection for polymer-modified RAP mixtures. A moderate dosage is more effective because it improves binder restoration and mixture durability without causing excessive softening, while rutting verification remains essential before field application. Full article

Overwintering peat fires are increasingly reported in the boreal regions, where they persist underground through winter and reignite in spring, intensifying greenhouse gas emissions and landscape degradation. This study investigates the conditions that enable peat fires to survive freezing and snow cover, and presents practical methods for their winter detection and suppression. We combined satellite data, UAV-based thermal imaging, time-lapse photography, and ground measurements of temperature, groundwater depth, and peat moisture to identify active overwintering hotspots. Our results show that these fires persist primarily where groundwater levels remain below 60 cm, particularly under tree roots, compacted soil, or elevated terrain that limits moisture recharge. UAV thermal imaging proved the most reliable detection tool, identifying 98% of hotspots. We developed and successfully applied a winter extinguishing method that involves mechanical disruption and dispersion of smoldering peat over frozen ground, allowing rapid cooling without re-ignition. These findings clarify the mechanisms sustaining overwintering fires and provide an effective approach for their mitigation, contributing to reduced emissions and improved management of boreal peatlands vulnerable to climate change. Full article

The rapid evolution of the Open Radio Access Network (O-RAN) architecture and the exponential growth in specification complexity create significant barriers for researchers translating 5G standards into practical implementations. Existing evaluation frameworks for large language models, such as ORAN-Bench-13K, focus predominantly on the theoretical comprehension of regulatory documents while neglecting the critical aspect of software execution. This disparity results in a profound semantic gap, defined here as the structural and conceptual misalignment between abstract normative requirements and their concrete realization in the source code of open platforms like srsRAN. To bridge this divide and enable advanced cognitive reasoning, this paper presents a Hybrid Retrieval-Augmented Generation (RAG) framework designed to unify two heterogeneous knowledge domains: the O-RAN/3GPP specification corpus and the srsRAN C++ codebase. The proposed architecture leverages a hierarchical Parent–Child Chunking strategy to preserve the structural integrity of complex code and normative protocols. Additionally, it introduces a probabilistic Semantic Query Routing mechanism that dynamically selects the relevant context domain based on query intent. This routing actively mitigates semantic interference—a phenomenon where merging conflicting cross-domain terminology introduces informational noise, which our baseline tests showed degrades response accuracy by 4.7%. Empirical evaluation demonstrates that the hybrid approach successfully overcomes this, achieving an overall accuracy of 76.70% and outperforming the standard RAG baseline of 72.00%. Furthermore, system performance analysis reveals that effective context filtering reduces the average response generation latency to 3.47 s, compared to 3.73 s for traditional RAG methods, rendering the framework highly suitable for real-time telecommunications engineering tasks. Full article

Remote sensing (RS) images are often degraded by atmospheric haze, which compromises both visual interpretation and downstream applications. To address this, we introduce CSGL-Former, a novel Cross-Stripes Global–Local Fusion Transformer for RS image dehazing. Our model efficiently captures anisotropic long-range dependencies using cross-stripes attention (CSA) and aggregates hierarchical global semantics via a Multi-Layer Global Aggregation (MLGA) module. In the decoder, global context is adaptively blended with fine-grained local features to restore intricate textures. Finally, inspired by the atmospheric scattering model, a soft reconstruction head restores the clear image by predicting spatially varying affine parameters, strictly preserving content fidelity while effectively removing haze. Trained end-to-end, CSGL-Former demonstrates a compelling balance of accuracy and efficiency. Extensive experiments on the RRSHID and SateHaze1K benchmarks show that our model achieves state-of-the-art or highly competitive performance against representative baselines. Ablation studies further validate the effectiveness of each proposed component. Full article

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor $\alpha \left(t\right)$, and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold $\epsilon$. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at $T_{CRC}=17.0$ s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. Full article