Search Results (1,011)

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

CFRP composite laminates have been widely used in shipbuilding and marine engineering fields, but there is currently a lack of comparative analysis of their blast resistance and dynamic performance under different anisotropic and load conditions. This study aims to characterize the damage response of thick composite laminates with different impact strengths, layer orientations, and laminate thicknesses under water-based explosive loads. By conducting underwater impact tests on laminated panels and combining fluid structure coupling simulations, the study focuses on understanding the deformation and failure mechanisms and quantifying the damage caused by structural properties and loading rates. The results show that while composite laminates show elastic deformation and high recoverability, they are susceptible to matrix tensile damage, particularly at edges and centers. This study reveals that maximum out-of-plane displacement is proportional to impact intensity, while damage dissipation energy is quadratically related. Optimal ply orientations can reduce anisotropy and mitigate damage. Increasing laminate thickness from 3 mm to 8 mm reduces the maximum out-of-plane displacement by 32%, with diminishing returns observed beyond 6 mm thickness. This research offers valuable insights for optimizing composite laminate design to enhance impact resistance and efficiency. Full article

Wind turbine blade damage detection is crucial for advancing wind energy as a sustainable alternative to fossil fuels. Existing methods based on image processing technologies face challenges such as limited adaptability to complex environments, trade-offs between model accuracy and computational efficiency, and inadequate real-time inference capabilities. In response to these limitations, we put forward MSWindD-YOLO, a lightweight real-time detection model for wind turbine blade damage. Building upon YOLOv5s, our work introduces three key improvements: (1) the replacement of the Focus module with the Stem module to enhance computational efficiency and multi-scale feature fusion, integrating EfficientNetV2 structures for improved feature extraction and lightweight design, while retaining the SPPF module for multi-scale context awareness; (2) the substitution of the C3 module with the GBC3-FEA module to reduce computational redundancy, coupled with the incorporation of the CBAM attention mechanism at the neck network’s terminus to amplify critical features; and (3) the adoption of Shape-IoU loss function instead of CIoU loss function to facilitate faster model convergence and enhance localization accuracy. Evaluated on the Wind Turbine Blade Damage Visual Analysis Dataset (WTBDVA), MSWindD-YOLO achieves a precision of 95.9%, a recall of 96.3%, an mAP@0.5 of 93.7%, and an mAP@0.5:0.95 of 87.5%. With a compact size of 3.12 MB and 22.4 GFLOPs inference cost, it maintains high efficiency. After TensorRT acceleration on Jetson Orin NX, the model attains 43 FPS under FP16 quantization for real-time damage detection. Consequently, the proposed MSWindD-YOLO model not only elevates detection accuracy and inference efficiency but also achieves significant model compression. Its deployment-compatible performance in edge environments fulfills stringent industrial demands, ultimately advancing sustainable wind energy operations through lightweight lifecycle maintenance solutions for wind farms. Full article

While insulator integrity is critical for power grid stability, prevailing detection algorithms often rely on computationally intensive models incompatible with resource-constrained edge devices like unmanned aerial vehicles (UAVs). Key limitations—including redundant feature interference, inadequate sensitivity to small targets, rigid fusion weights, and sample imbalance—further restrict practical deployment. To address those problems, this study presents a lightweight insulator anomaly detection algorithm, LAI-YOLO. First, the SqueezeGate-C3k2 (SG-C3k2) module, equipped with an adaptive gating mechanism, is incorporated into the Backbone network to reduce redundant information during feature extraction. Secondly, we propose a High-level Screening–Feature Weighted Feature Pyramid Network (HS-WFPN) to replace FPN+PAN via selective weighted feature fusion, enabling dynamic cross-scale integration and enhanced small-target detection. Then, a reconstructed lightweight detection head coupled with Slide Weighted Focaler Loss (SWFocalerLoss) mitigates performance degradation from sample imbalance. Ultimately, the layer adaptation for the magnitude-based pruning (LAMP) technique slashes computational demands without sacrificing detection prowess. Experimental results on our insulator anomaly dataset demonstrate that the improved model achieves higher efficacy in identifying insulator anomalies, with mAP@0.5 increasing from 88.2% to 91.1%, while model parameters and FLOPs are diminished to 45.7% and 53.9% of the baseline, respectively. This efficiency facilitates the deployment of edge devices and highlights the method’s considerable application potential. Full article

We experimentally investigate the structural, magnetic, transport, and electronic properties of two d⁵ iridate double perovskite materials La₂BIrO₆ (B = Mg, Zn). Notably, despite similar crystallographic structure, the two compounds show distinctly different magnetic behaviors. The M = Mg compound shows an antiferromagnetic-like linear field-dependent isothermal magnetization below its transition temperature, whereas the M = Zn counterpart displays a clear hysteresis loop followed by a noticeable coercive field, indicative of ferromagnetic components arising from a non-collinear Ir spin arrangement. The local structure studies authenticate perceptible M/Ir antisite disorder in both systems, which complicates the magnetic exchange interaction scenario by introducing Ir-O-Ir superexchange pathways in addition to the nominal Ir-O-B-O-Ir super-superexchange interactions expected for an ideally ordered structure. While spin–orbit coupling (SOC) plays a crucial role in establishing insulating behavior for both these compounds, the rotational and tilting distortions of the IrO₆ (and MO₆) octahedral units further lift the ideal cubic symmetry. Finally, by measuring the Ir-L₃ edge resonant inelastic X-ray scattering (RIXS) spectra for both the compounds, giving evidence of spin–orbit-derived low-energy inter-J-state (intra t_2g) transitions (below ~1 eV), the charge transfer (O 2p → Ir 5d), and the crystal field (Ir t_2g → e_g) excitations, we put forward a qualitative argument for the interplay among effective SOC, non-cubic crystal field, and intersite hopping in these two compounds. Full article

Fetal movement monitoring (FMM) is crucial for assessing fetal well-being, traditionally relying on clinical assessments or maternal perception, each with inherent limitations. This study presents a novel lightweight deep learning framework for real-time FMM on edge devices. Data were collected from 120 participants using a wearable device equipped with an inertial measurement unit, which captured both accelerometer and gyroscope data, coupled with a rigorous two-stage labeling protocol integrating maternal perception and ultrasound validation. We addressed class imbalance using virtual-rotation-based augmentation and adaptive clustering-based undersampling. The data were transformed into spectrograms using the Short-Time Fourier Transform, serving as input for deep learning models. To ensure model efficiency suitable for resource-constrained microcontrollers, we employed knowledge distillation, transferring knowledge from larger, high-performing teacher models to compact student architectures. Post-training integer quantization further optimized the models, reducing the memory footprint by 74.8%. The final optimized model achieved a sensitivity (SEN) of 90.05%, a precision (PRE) of 87.29%, and an F1-score (F1) of 88.64%. Practical energy assessments showed continuous operation capability for approximately 25 h on a single battery charge. Our approach offers a practical framework adaptable to other medical monitoring tasks on edge devices, paving the way for improved prenatal care, especially in resource-limited settings. Full article

Land degradation in Iraq’s Mesopotamian plain threatens food security and rural livelihoods, yet the relative roles of climatic water deficits versus anthropogenic pressures remain poorly attributed in space. We test the hypothesis that multi-timescale climatic water deficits (SPEI-03/-06/-12) exert a stronger effect on vegetation degradation risk than anthropogenic pressures, conditional on hydrological connectivity and irrigation. Using Babil and Al-Qadisiyah (2000–2023) as a case, we implement a four-part pipeline: (i) Fractional Vegetation Cover with Mann–Kendall/Sen’s slope to quantify greening/browning trends; (ii) LandTrendr to extract disturbance timing and magnitude; (iii) annual LULC maps from a Random Forest classifier to resolve transitions; and (iv) an XGBoost classifier to map degradation risk and attribute climate vs. anthropogenic influence via drop-group permutation (ΔAUC), grouped SHAP shares, and leave-group-out ablation, all under spatial block cross-validation. Driver attribution shows mid-term and short-term drought (SPEI-06, SPEI-03) as the strongest predictors, and conditional permutation yields a larger average AUC loss for the climate block than for the anthropogenic block, while grouped SHAP shares are comparable between the two, and ablation suggests a neutral to weak anthropogenic edge. The XGBoost model attains AUC = 0.884 (test) and maps 9.7% of the area as high risk (>0.70), concentrated away from perennial water bodies. Over 2000–2023, LULC change indicates CA +515 km², HO +129 km², UL +70 km², BL −697 km², WB −16.7 km². Trend analysis shows recovery across 51.5% of the landscape (+29.6% dec⁻¹ median) and severe decline over 2.5% (−22.0% dec⁻¹). The integrated design couples trend mapping with driver attribution, clarifying how compounded climatic stress and intensive land use shape contemporary desertification risk and providing spatial priorities for restoration and adaptive water management. Full article

This study examines the effects of partial wakes caused by upstream turbine yaw control on the trailing-edge noise of a downstream turbine under stable and neutral atmospheric conditions. Using a combined model coupling the unsteady vortex lattice method (UVLM) with the Curl wake model, calibrated with large eddy simulation data, wake behavior and noise characteristics were analyzed for yaw angles from −30° to +30°. Results show that partial wakes slightly raise overall noise levels and lateral asymmetry of trailing-edge noise, while amplitude modulation (AM) strength is more strongly influenced by yaw control. AM varies linearly with wake deflection at moderate yaw angles but behaves nonlinearly beyond a threshold due to large wake deflection and deformation. Findings reveal that yaw control can significantly increase the lateral asymmetry in the AM strength directivity pattern of the downstream turbine, and that AM characteristics depend on the complex interplay between inflow distribution and convective amplification effects, highlighting the importance of accurate wake prediction, along with appropriate consideration of observer point location and blade rotation, for evaluating AM characteristics of a wind turbine influenced by a partial wake. Full article

Urban agriculture in space-constrained cities requires compact, reproducible propagation systems. Therefore, the aim of this Technical Note is to design, implement, and functionally validate a low-cost, modular hydroponic chamber (SSHG) for early-stage vegetative propagation. This system couples DHT11-based temperature/RH monitoring with rule-based actuation—irrigation 4×/day and temperature-triggered ventilation—under the control of an Arduino Uno microcontroller; LED lighting was not controlled nor analyzed. Two 15-day trials with basil (Ocimum basilicum L.) yielded rooting rates of 61.7% (37/60) and 43.3% (26/60) under a deliberate minimal-input configuration without nutrient solutions or rooting hormones. Environmental summaries and spatial survival maps revealed edge-effect patterns and RH variability that inform irrigation layout improvements. The chamber, bill of materials, and protocol are documented to support replication and iteration. Thus, the SSHG provides a transferable baseline for educators and researchers to audit, reproduce, and improve small-footprint, controlled-environment propagation. Beyond its technical feasibility, the SSHG contributes to sustainability by leveraging low-cost, readily available components, enabling decentralized seedling production in space-constrained settings, and operating under a minimal-input configuration. In line with widely reported hydroponic efficiencies (e.g., lower water use relative to soil-based propagation), this open and replicable platform aligns with SDGs 2, 11, 12, and 13. Full article

Cracking in asphalt layers of rigid–flexible composite pavements under coupled ambient temperature fields and traffic loading represents a critical failure mode. Traditional models based on uniform temperature assumptions inadequately capture the crack propagation mechanisms. This study developed a thermo-mechanical coupling model that incorporates realistic temperature-modulus gradients to analyze the compressive-shear behavior and simulate crack propagation using the extended finite element method (XFEM) coupled with a modified Paris’ law. Key findings reveal that the asphalt layer exhibits a predominant compressive-shear stress state; increasing the base modulus from 10,000 MPa to 30,000 MPa reduces the maximum shear stress by 22.8% at the tire centerline and 8.6% at the edge; thermal stress predominantly drives crack initiation, whereas vehicle loading governs the propagation path; field validation via cored samples confirms inclined top-down cracking under thermo-mechanical coupling; and the fracture energy release rate (G_f) reaches a minimum of 155 J·m⁻² at 14:00, corresponding to a maximum fatigue life of 32,625 cycles, and peaks at 350 J·m⁻² at 01:00, resulting in a reduced life of 29,933 cycles—reflecting a 9.0% temperature-induced fatigue life variation. The proposed model, which integrates non-uniform temperature gradients, offers enhanced accuracy in capturing complex boundary conditions and stress states, providing a more reliable tool for durability design and assessment of composite pavements. Full article

The instance segmentation of power distribution equipment in infrared images is a prerequisite for determining its overheating fault, which is crucial for urban power grids. Due to the specific characteristics of power distribution equipment, the objects are generally characterized by small scale and complex structure. Existing methods typically use a backbone network to extract features from infrared images. However, the inherent down-sampling operations lead to information loss of objects. The content regions of small-scale objects are compressed, and the edge regions of complex-structured objects are fragmented. In this paper, (1) the first unmanned aerial vehicle-based infrared dataset PDI for power distribution inspection is constructed with 16,596 images, 126,570 instances, and 7 categories of power equipment. It has the advantages of large data volume, rich geographic scenarios, and diverse object patterns, as well as challenges of distribution imbalance, category imbalance, and scale imbalance of objects. (2) A reconstruction error (RE)-guided instance segmentation framework, coupled with an object reconstruction decoder (ORD) and a difference feature enhancement (DFE) module, is proposed. The former reconstructs the objects, where the reconstruction result indicates the position and degree of information loss of the objects. Therefore, the difference map between the reconstruction result and the input image effectively replays the object features. The latter adaptively compensates for object features by global fusion between the difference features and backbone features, thereby enhancing the spatial representation of objects. Extensive experiments on the constructed and publicly available datasets demonstrate the strong generalization, superiority, and versatility of the proposed framework. Full article

In this paper, a full-domain stochastic response analysis is performed based on the meshless method to reveal the time–frequency dynamic characteristics, including the power spectral density (PSD) responses in the frequency domain and the evolving PSD distribution in the time domain for a sandwich trapezoidal plate–shell coupled system. The general governing equations are derived based on the first-order shear deformation theory (FSDT), linear piston theory and Hamilton’s principle, and the stochastic excitation is integrated into the meshless framework based on the pseudo-excitation method (PEM). By constructing the meshless shape function covering the entire structural domain from Chebyshev polynomials and discretizing the continuous domain into a series of nodes within a square definition domain, the points are assembled according to the sequence number and the equilibrium relationship on the coupling edge to obtain the overall vibration equations. The validity is demonstrated by matching the mode shapes, PSD responses, time history displacement and critical flutter boundaries with FEM simulation and reported data. Finally, the time–frequency characteristics of each substructure under global and single stochastic excitation, and the effect of aerodynamic pressure on full-domain stochastic vibration, are revealed. Full article

This study elucidated the evolution and catastrophic failure mechanisms of concrete’s mechanical properties under high-temperature and moisture-coupled environments. Specimens underwent hygrothermal shock simulation via constant-temperature drying (100 °C/200 °C, 4 h) followed by water quenching (20 °C, 30 min). Uniaxial compression tests were performed using a uniaxial compression test machine with synchronized multi-scale damage monitoring that integrated digital image correlation (DIC), acoustic emission (AE), and infrared thermography. The results demonstrated that hygrothermal coupling reduced concrete ductility significantly, in which the peak strain decreased from 0.36% (ambient) to 0.25% for both the 100 °C and 200 °C groups, while compressive strength declined to 42.8 MPa (−2.9%) and 40.3 MPa (−8.6%), respectively, with elevated elastic modulus. DIC analysis revealed the temperature-dependent failure mode reconstruction: progressive end cracking (max strain 0.48%) at ambient temperature transitioned to coordinated dual-end cracking with jump-type damage (abrupt principal strain to 0.1%) at 100 °C and degenerated to brittle fracture oriented along a singular path (principal strain band 0.015%) at 200 °C. AE monitoring indicated drastically reduced micro-damage energy barriers at 200 °C, where cumulative energy (4000 mV·ms) plummeted to merely 2% of the ambient group (200,000 mV·ms). Infrared thermography showed that energy aggregation shifted from “centralized” (ambient) to “edge-to-center migration” (200 °C), with intensified thermal shock effects in fracture zones (ΔT ≈ −7.2 °C). The study established that hygrothermal coupling weakens the aggregate-paste interfacial transition zone (ITZ) by concentrating the strain energy along singular weak paths and inducing brittle failure mode degeneration, which thereby provides theoretical foundations for fire-resistant design and catastrophic failure warning systems in concrete structures exposed to coupled environmental stressors. Full article

The lossof ear tags in breeding pigs can lead to the loss or confusion of individual identity information. Timely and accurate detection, along with continuous tracking of breeding pigs that have lost their ear tags, is crucial for improving the precision of farm management. However, considering the real-time requirements for the detection of ear tag-lost breeding pigs, coupled with tracking challenges such as similar appearances, clustered occlusion, and rapid movements of breeding pigs, this paper proposed a dual-view synergistic method for detecting ear tag-lost breeding pigs and tracking individuals. First, a lightweight ear tag loss detector was developed by combining the Cascade-TagLossDetector with a channel pruning algorithm. Second, a synergistic architecture was designed that integrates a localized top-down view with a panoramic oblique view, where the detection results of ear tag-lost breeding pigs from the localized top-down view were mapped to the panoramic oblique view for precise localization. Finally, an enhanced tracker incorporating Motion Attention was proposed to continuously track the localized ear tag-lost breeding pigs. Experimental results indicated that, during the ear tag loss detection stage for breeding pigs, the pruned detector achieved a mean average precision of 94.03% for bounding box detection and 90.16% for instance segmentation, with a parameter count of 28.04 million and a detection speed of 37.71 fps. Compared to the unpruned model, the parameter count was reduced by 20.93 million, and the detection speed increased by 12.38 fps while maintaining detection accuracy. In the tracking stage, the success rate, normalized precision, and precision of the proposed tracker reached 86.91%, 92.68%, and 89.74%, respectively, representing improvements of 4.39, 3.22, and 4.77 percentage points, respectively, compared to the baseline model. These results validated the advantages of the proposed method in terms of detection timeliness, tracking continuity, and feasibility of deployment on edge devices, providing significant reference value for managing livestock identity in breeding farms. Full article

High-power nitride-based edge-emitting lasers with low-divergence Gaussian beams are useful for applications including laser surgery, material processing, and 3D printing. Fundamental lateral mode operation is typically achieved using narrow or shallow ridges. However, narrow ridges limit the active region, while shallow ridges can allow higher-order mode lasing. To address these challenges, this study applies a supersymmetry approach using optical coupling between neighbouring ridges to confine the fundamental mode while suppressing higher-order modes. Two nitride-based edge-emitting laser configurations—double-ridge and triple-ridge waveguides—are analysed, with a focus on ridge-width tolerances and the effects of gain and absorption. Both configurations achieve strong mode discrimination. However, the triple-ridge waveguide structure exhibits a mode separation ratio more than twice that of the double-ridge waveguide, making it promising for high-power single-mode operation. The results of this study provide a basis for further study of supersymmetry effects in nitride lasers. Full article