Search Results (229)

Detecting ships in Synthetic Aperture Radar (SAR) images poses a complex challenge, with recent progress primarily attributed to the development of rotated detectors. However, existing methods often neglect the crucial influence of inherent characteristics in SAR images, such as common speckle noise. Moreover, a notable gap exists in modeling diverse features, particularly the fusion of rotational and high-frequency features. To address these challenges, this paper introduces a high-accuracy detector called PRDet, which builds on two key innovations: partial differential equation (PDE)-Guided Wavelet Transform (PGWT) and Diverse Feature Learning Block (DFLB). The PGWT enhances high-frequency features, such as edges and textures, while eliminating speckle noise by optimizing wavelet transform with PDE, leveraging the ability of PDE to model local variations and preserve structural details. The DFLB, with strong expressive capability, extracts and fuses multi-form ship features through three branches, enabling more accurate ship localization. Extensive experimental evaluations on the publicly available RSSDD and SRSDD-V1.0 benchmarks demonstrate PRDet’s superiority over other SAR rotated ship detectors. For example, on the RSSDD dataset, PRDet achieves an offshore precision of 0.938 and an mAP of 0.908, confirming its effectiveness for practical maritime surveillance applications. Full article

This work reports the synthesis and characterization of Carbon Quantum Dots (CQDs) embedded in an organic–inorganic hybrid SiO₂: PMMA matrix, designed as a novel plastic scintillator material. The CQDs were synthesized through a solvo-hydrothermal method and incorporated using a sol–gel polymerization process, resulting in a mechanically durable and optically active hybrid. Structural analysis with X-ray diffraction and TEM confirmed crystalline quantum dots approximately 10 nm in size. Extensive optical characterization, including band gap measurement, photoluminescence under 325 nm UV excitation, lifetime evaluations, and quantum yield measurement, revealed a blue emission centered at 426 nm with a decay time of 3–3.6 ns. The hybrid scintillator was integrated into a compact cosmic ray detector using a photomultiplier tube optimized for 420 nm detection. The system effectively detected secondary atmospheric muons produced by low-energy cosmic rays, validated through the vertical equivalent muon (VEM) technique. These findings highlight the potential of CQD-based hybrid materials for advanced optical sensing and scintillation applications in complex environments, supporting the development of compact and sensitive detection systems. Full article

The red soil region in southern China is an ecologically fragile area. Although ecological engineering construction has achieved phased results, there are still obvious gaps in research on the mechanisms underlying vegetation dynamics in response to natural and anthropogenic variables. Changting County (CTC) serves as a typical case of vegetation degradation and restoration in the region. We examined the vegetation dynamics in CTC with the fraction vegetation cover (FVC) based on kernel normalized difference vegetation index-based dimidiate pixel model (kNDVI-DPM) and employed the optimal parameter-based geographical detector (OPGD), multiscale geographically weighted regression (MGWR), and partial least square structural equation modeling (PLS-SEM) to analyze interaction mechanisms between vegetation dynamics and underlying factors. The FVC showed a fluctuating upward trend at a rate of 0.0065 yr⁻¹ (p < 0.001) from 2000 to 2020. The spatial distribution pattern was high in the west and low in the east. Soil and terrain factors were the primary factors dominating the spatial heterogeneity of FVC, soil organic matter and elevation showing the most significant influence, with annual mean q-values of 0.4 and 0.3, respectively. Climate, terrain, and soil properties positively and anthropogenic activities negatively impacted vegetation. From 2000 to 2020, the path coefficient of anthropogenic activities to FVC decreases from −0.152 to −0.045, the adverse effects of human activities are diminishing with ongoing ecological construction efforts. Climate and anthropogenic activities act indirectly on vegetation through negative effects on soils and terrain. The impact of climate on soils and terrain is gradually lessening, whilst the influence of anthropogenic activities continues to grow. This study provides an analytical framework for understanding the complex interrelationships between vegetation changes and the underlying factors. Full article

AUVs offer the potential for in situ MP detection at constant, pre-set depths in marine environments. By carrying onboard MP detectors, AUVs can serve as alternatives to traditional methods of sample collection, processing, and analysis, while also addressing the inefficiencies and complexities associated with conventional detection procedures. This study conducts a comprehensive review of existing and potential MP detection methods that can be integrated with AUVs for in situ detection. In particular, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review analyzes selected studies on MP detection using AUVs. It finds that real-time, in situ MP detection via AUVs or multi-AUV systems remains underdeveloped. Key challenges include deep-sea communication, sensor integration, and underwater durability. The review highlights the current advances, research gaps, and future directions for AUV-based MP detection technologies. Full article

Accurate and automated detection of diseases is crucial for sustainable strawberry production. However, the challenges posed by small size, mutual occlusion, and high intra-class variance of symptoms in complex agricultural environments make this difficult. Mainstream deep learning detectors often do not perform well under these demanding conditions. We propose a novel detection framework designed for superior accuracy and robustness to address this critical gap. Our framework introduces four key innovations: First, we propose a novel attention-driven detection head featuring our Parallel Pyramid Attention (PPA) module. Inspired by pyramid attention principles, our module’s unique parallel multi-branch architecture is designed to overcome the limitations of serial processing. It simultaneously integrates global, local, and serial features to generate a fine-grained attention map, significantly improving the model’s focus on targets of varying scales. Second, we enhance the core feature fusion blocks by integrating Monte Carlo Attention (MCAttn), effectively empowering the model to recognize targets across diverse scales. Third, to improve the feature representation capacity of the backbone without increasing the parametric overhead, we replace standard convolutions with Frequency-Dynamic Convolutions (FDConv). This approach constructs highly diverse kernels in the frequency domain. Finally, we employ the Scale-Decoupled Loss function to optimize training dynamics. By adaptively re-weighting the localization and scale losses based on target size, we stabilize the training process and improve the Precision of bounding box regression for small objects. Extensive experiments on a challenging dataset related to strawberry diseases demonstrate that our proposed model achieves a mean Average Precision (MAP) of 81.1%. This represents an improvement of 2.1% over the strong YOLOv12-n baseline, highlighting its practical value as an effective tool for intelligent disease protection. Full article

Target detection is a cornerstone task in hyperspectral image processing but faces significant challenges due to domain gaps. While statistical detectors like Constrained Energy Minimization (CEM) and Adaptive Cosine Estimator (ACE) are not prone to learned biases, in practice they still suffer from mismatches between the reference target spectrum and the spectral characteristics of the target in the test scene. We propose Test-time Adaptive Spectrum Refinement (TASR), a novel framework addressing this problem. TASR operates in an interpretable, lightweight, data-efficient manner, requiring only a single labeled source image of the target material. At test time, TASR dynamically refines the target spectrum to better align with the spectral properties of the test scene. This adaptive refinement enables detectors to effectively handle data with spectral variations, bridging the gap between the source and test spectra. To validate TASR, we conduct extensive experiments on established benchmarks and introduce a new dataset—ShadySunnyDiffuse (SSD)—which explicitly tests detector robustness to naturally occurring illumination changes. We further demonstrate the method’s versatility by applying it to camouflage detection and show compatibility with multiple statistical detectors. Our results establish TASR as a state-of-the-art approach in domain-adaptive hyperspectral target detection and target spectrum management. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Frequency-hopping multiple access is widely adopted to blunt narrow-band jamming and limit spectral disclosure in cyber–physical systems, yet its practical resilience depends on three sequence-level properties. First, balancedness guarantees that every carrier is occupied equally often, removing spectral peaks that a jammer or energy detector could exploit. Second, a wide gap between successive hops forces any interferer to re-tune after corrupting at most one symbol, thereby containing error bursts. Third, a no-hit zone (NHZ) window with a zero pairwise Hamming correlation eliminates user collisions and self-interference when chip-level timing offsets fall inside the window. This work introduces an algebraic construction that meets the full set of requirements in a single framework. By threading a permutation over an integer ring and partitioning the period into congruent sub-blocks tied to the desired NHZ width, we generate balanced wide gap no-hit zone frequency-hopping (WG-NHZ FH) sequence sets. Analytical proofs show that (i) each sequence achieves the Lempel–Greenberger bound for auto-correlation, (ii) the family and zone sizes satisfy the Ye–Fan bound with equality, (iii) the hop-to-hop distance satisfies a provable WG condition, and (iv) balancedness holds exactly for every carrier frequency. Full article

Space-based gravitational wave detectors, such as the Laser Interferometer Space Antenna (LISA) and Taiji, will observe GWs from $O\left({10}^8\right)$ galactic binary systems, allowing a completely unobscured view of the Milky Way structure. While previous studies have established theoretical expectations based on idealized data-analysis methods that use the true catalog of sources, we present an end-to-end analysis pipeline for inferring galactic structure parameters based on the detector output alone. We employ the GBSIEVER algorithm to extract GB signals from LISA Data Challenge data and develop a maximum likelihood approach to estimate a bulge-disk galactic model using the resolved GBs. We introduce a two-tiered selection methodology, combining frequency derivative thresholding and proximity criteria, to address the systematic overestimation of frequency derivatives that compromises distance measurements. We quantify the performance of our pipeline in recovering key Galactic structure parameters and the potential biases introduced by neglecting the errors in estimating the parameters of individual GBs. Our methodology represents a step forward in developing practical techniques that bridge the gap between theoretical possibilities and observational implementation. Full article

In order to address the pressing challenge posed by the proliferation of fake news in the digital age, we emphasize its profound and harmful impact on societal structures, including the misguidance of public opinion, the erosion of social trust, and the exacerbation of social polarization. Current fake news detection methods are largely limited to superficial text analysis or basic text–image integration, which face significant limitations in accurately identifying deceptive information. To bridge this gap, we propose the UC-CMAF framework, which comprehensively integrates news text, images, and user comments through an adaptive co-attention fusion mechanism. The UC-CMAF workflow consists of four key subprocesses: multimodal feature extraction, cross-modal adaptive collaborative attention fusion of news text and images, cross-modal attention fusion of user comments with news text and images, and finally, input of fusion features into a fake news detector. Specifically, we introduce multi-head cross-modal attention heatmaps and comment importance visualizations to provide interpretability support for the model’s predictions, revealing key semantic areas and user perspectives that influence judgments. Through the cross-modal adaptive collaborative attention mechanism, UC-CMAF achieves deep semantic alignment between news text and images and uses social signals from user comments to build an enhanced credibility evaluation path, offering a new paradigm for interpretable fake information detection. Experimental results demonstrate that UC-CMAF consistently outperforms 15 baseline models across two benchmark datasets, achieving F1 Scores of 0.894 and 0.909. These results validate the effectiveness of its adaptive cross-modal attention mechanism and the incorporation of user comments in enhancing both detection accuracy and interpretability. Full article

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

Research on the supply–demand relationships of ecosystem services (ESs) in alpine pastoral regions remains relatively scarce, yet it is crucial for regional ecological management and sustainable development. This study focuses on the Sanjiangyuan Region, a typical alpine pastoral area and significant ecological barrier, to quantitatively assess the supply–demand dynamics of key ESs and their spatial heterogeneity from 2000 to 2020. It further aims to elucidate the underlying driving mechanisms, thereby providing a scientific basis for optimizing regional ecological management. Four key ES indicators were selected: water yield (WY), grass yield (GY), soil conservation (SC), and habitat quality (HQ). ES supply and demand were quantified using an integrated approach incorporating the InVEST model, the Revised Universal Soil Loss Equation (RUSLE), and spatial analysis techniques. Building on this, the spatial patterns and temporal evolution characteristics of ES supply–demand relationships were analyzed. Subsequently, the Geographic Detector Model (GDM) and Geographically and Temporally Weighted Regression (GTWR) model were employed to identify key drivers influencing changes in the comprehensive ES supply–demand ratio. The results revealed the following: (1) Spatial Patterns: Overall ES supply capacity exhibited a spatial differentiation characterized by “higher values in the southeast and lower values in the northwest.” Areas of high ES demand were primarily concentrated in the densely populated eastern region. WY, SC, and HQ generally exhibited a surplus state, whereas GY showed supply falling short of demand in the densely populated eastern areas. (2) Temporal Dynamics: Between 2000 and 2020, the supply–demand ratios of WY and SC displayed a fluctuating downward trend. The HQ ratio remained relatively stable, while the GY ratio showed a significant and continuous upward trend, indicating positive outcomes from regional grass–livestock balance policies. (3) Driving Mechanisms: Climate and natural factors were the dominant drivers of changes in the ES supply–demand ratio. Analysis using the Geographical Detector’s q-statistic identified fractional vegetation cover (FVC, q = 0.72), annual precipitation (PR, q = 0.63), and human disturbance intensity (HD, q = 0.38) as the top three most influential factors. This study systematically reveals the spatial heterogeneity characteristics, dynamic evolution patterns, and core driving mechanisms of ES supply and demand in an alpine pastoral region, addressing a significant research gap. The findings not only provide a reference for ES supply–demand assessment in similar regions regarding indicator selection and methodology but also offer direct scientific support for precisely identifying priority areas for ecological conservation and restoration, optimizing grass–livestock balance management, and enhancing ecosystem sustainability within the Sanjiangyuan Region. Full article

Conjugated polymers (CPs) offer many attractive features for photodiodes and photovoltaics, including solution processability, ease of scale-up, light weight, low cost, and mechanical flexibility. CPs have a wide range of energy gaps; thus, the choice of the specific polymer determines the optimum operational wavelength range. However, there are relatively few CPs with a strong absorption in the blue region of the spectrum where the human eye is most sensitive (440 to 470 nm) and none with an energy gap at 2.75 eV (450 nm), which corresponds to the peak of the CIE-1931 z(λ) color-matching function and the dominant blue light emission wavelength in computer and smartphone displays. Blue-light detectors in this wavelength range are important for light hazard control, sky polarization studies, and for blue-light information devices, where 450 nm corresponds to the principal emission of GaN-based light sources. We report on a new CP called Atums Green (AG), which shows promising characteristics as a blue-light photodetection polymer optimized for exactly this range of wavelengths centered around 450 nm. We built and measured a simple photodetector made from spin-coated films of AG and showed that its photosensitivity can be improved by the addition of asphaltene, a low-cost carbonaceous waste product. Full article

X-ray ptychography is an ultrahigh resolution imaging technique widely used in synchrotron radiation facilities. Its imaging performance relies on the quality of the acquired signals. However, the X-ray detectors used often suffer from signal loss due to sensor gaps, beamstops, defective pixels, overexposure, or other factors, resulting in degraded image quality. To suppress and compensate for the effects of signal loss, we proposed the known probe approach to partially recover the lost signals and introduced the high probe divergence strategy by investigating the effects of probe divergence on reconstruction quality under signal loss conditions. Both simulation and experiment results show that high probe divergence can effectively suppress the impact of signal loss on reconstruction quality while using a known probe as the initial probe for reconstruction can largely recover missing signals in Fourier space, resulting in a much better image than using a guessed initial probe. These strategies allow for high-quality imaging in the presence of signal loss without secondary data acquisition, significantly improving experimental efficiency and reducing radiation damage compared to previous strategies. Full article

Skin aging is a complex process influenced by several factors, including UV exposure, environmental stressors, and lifestyle choices. The demand for effective, natural skincare products has driven research into plant-based oils rich in bioactive compounds. Rosehip oil has garnered attention for its high content of carotenoids, phenolics, and antioxidants, which are known for their anti-aging, photoprotective, and skin-rejuvenating properties. Despite the growing interest in rosehip oil, limited studies have investigated its efficacy on human skin using advanced imaging technologies. This study aims to fill this gap by evaluating the efficacy of cold-pressed Rosa canina seed oil on facial skin characteristics, specifically wrinkles, ultraviolet (UV) spot reduction, and erythema mitigation, using imaging technologies (the VISIA analysis system). Seed oil pressed from R. canina collected from the Băișoara area of Cluj County has been selected for this study due to its high carotenoid, phenolic, and antioxidant contents. The oil has also been analyzed for the content of individual carotenoids (i.e., lutein, lycopene, β Carotene, and zeaxanthin) using HPLC-DAD (High-Performance Liquid Chromatography—Diode Array Detector), along with lutein and zeaxanthin esters and diesters. After the preliminary screening of multiple Rosa species for carotenoid, phenolic, and antioxidant contents, the R. canina sample with the highest therapeutic potential was selected. A cohort of 27 volunteers (aged 30–65) underwent a five-week treatment protocol, wherein three drops of the selected rosehip oil were topically applied to the face daily. The VISIA imaging was conducted before and after the treatment to evaluate changes in skin parameters, including the wrinkle depth, UV-induced spots, porphyrins, and texture. Regarding the bioactivities, rosehip oil showed a significant total carotenoids content (28.398 μg/mL), with the highest levels in the case of the β-carotene (4.49 μg/mL), lutein (4.33 μg/mL), and zexanthin (10.88 μg/mL) contents. Results indicated a significant reduction in mean wrinkle scores across several age groups, with notable improvements in individuals with deeper baseline wrinkles. UV spots also showed visible declines, suggesting ideal photoprotective and anti-pigmentary effects attributable to the oil’s high vitamin A and carotenoid content. Porphyrin levels, often correlated with bacterial activity, decreased in most subjects, hinting at an additional antimicrobial or microbiome-modulatory property. However, skin responses varied, possibly due to individual differences in skin sensitivity, environmental factors, or compliance with sun protection. Overall, the topical application of R. canina oil appeared to improve the facial skin quality, reduce the appearance of age-related markers, and support skin health. These findings reinforce the potential use of rosehip oil in anti-aging skincare formulations. Further long-term, large-scale studies are warranted to refine dosing regimens, investigate mechanisms of action, and explore synergistic effects with other bioactive compounds. Full article