Search Results (63)

Silver (Ag) and gold (Au) nanoparticles (NPs) are widely used in biomedicine, electronics, and catalysis, but their potential toxicity raises occupational health concerns. This study assessed the cytotoxicity and cellular interactions of Ag and Au NPs in human bronchial epithelial cells (BEAS-2B) using a standardized OECD three-tiered approach, alongside characterization of lung-deposited surface area (LDSA) concentrations during NP synthesis, which remained within ranges typically reported in occupational environments. Transmission electron microscopy revealed that AgNPs formed irregular clusters (~8.7 nm primary size, >30 nm aggregates), whereas AuNPs remained spherical (~13.4 nm). Real-time cytotoxicity analysis (xCELLigence) showed acute toxicity of AgNPs at 5 μg/cm², while AuNPs exhibited no cytotoxic effects. Dark-field and 3D hyperspectral imaging demonstrated that some AgNPs were internalized by BEAS-2B cells, whereas AuNPs remained mostly on the cell surface, indicating that uptake alone does not determine cytotoxicity. The greater dissolution potential of AgNPs and possible release of Ag⁺ ions may contribute to the enhanced cytotoxic effects observed in comparison to AuNPs, as suggested in previous studies. Although oxidative stress, mitochondrial dysfunction, and related cellular mechanisms were not directly assessed in the present study, the findings demonstrate differential cellular responses following nanoparticle exposure under realistic occupational exposure conditions. These results contribute to understanding nanoparticle–cell interactions and support the need for further mechanistic investigations to inform safer nanomaterial use. Full article

Crop leaf disease segmentation in complex natural environments remains challenging because lesion regions often exhibit substantial scale variation, blurred boundaries, and severe background interference. To address these issues, this study proposes a Multi-Scale Feature Rectification Network (MFR-Net) for crop leaf disease segmentation. The proposed network adopts an EfficientNetV2-S-based encoder to extract hierarchical features, incorporates a hybrid attention mechanism to enhance lesion-sensitive spatial and channel representations, introduces a Cross-Window Atrous Spatial Pyramid Pooling (CWASPP) module to strengthen multi-scale contextual modeling, and employs a Feature Rectification Module (FRM) in the decoder to alleviate semantic inconsistency during cross-level feature fusion. Experiments on a Kaggle-derived benchmark constructed from the unaugmented data folder of the public Leaf Disease Segmentation Dataset, containing 588 diseased-leaf images and 588 corresponding binary lesion masks, showed that MFR-Net achieved the highest mIoU of 74.27% and the highest Recall of 87.61% among the compared methods, and maintained competitive Dice performance (84.25%) with 25.10 M parameters and 37.55 G FLOPs. Ablation results further confirmed the effectiveness of the proposed design, with CWASPP providing the most notable individual contribution. Additional experiments were conducted on an independent Apple Leaf Dataset comprising 3197 image–mask pairs, collected under mixed controlled and natural field-like imaging conditions. The results showed competitive performance under a different data distribution, and robustness evaluation further verified stable performance under severe noise, blur, darkness, and contrast variation. All experiments were implemented in PyTorch 2.11.0 (CUDA 12.8) on a workstation equipped with an NVIDIA GeForce RTX 4060 Ti GPU (8 GB). These results indicate that MFR-Net provides an effective and robust solution for crop leaf disease segmentation in complex agricultural scenarios. Full article

Object detection technology has been widely applied in fields such as autonomous driving and security surveillance, where it serves as a vital component of intelligent systems. However, under adverse hazy weather conditions, objects obscured by haze and their edges may lose detailed visual information. Existing object detection methods lack targeted mechanisms to address these challenges, resulting in a marked decline in detection accuracy in complex environments. To this end, this paper proposes an object detection method based on an end-to-end robust detection framework, termed the Physics-informed Frequency-Enhanced Network (PFE-Net). We propose a physics-guided visibility enhancement module (PG-VEM) that leverages the atmospheric scattering model and dark channel prior to adaptively compensate for degraded image features under adverse weather conditions, thereby restoring image details and contrast. Meanwhile, the frequency-domain edge-awareness module (FD-EPM) explicitly enhances the geometric contours of blur-obscured objects through Fourier transform and high-pass filtering, thereby improving the discriminability of edge features. Comprehensive experiments were conducted on both the real-world RTTS dataset and the synthetic VOChaze dataset to validate the effectiveness of the proposed approach. The results indicate that the proposed method achieves significant improvements in accuracy, particularly under complex weather conditions, demonstrating excellent all-weather environmental perception capabilities. This method has important practical engineering value and can substantially enhance the safety of autonomous driving and security surveillance systems under adverse weather conditions. Full article

Van der Waals (vdW) heterostructures are attractive for optoelectronic devices due to their lattice-mismatch tolerance and tunable band structures. Here, we report a gate-tunable Au/MoS₂/WSe₂ dual junction photodetector featuring competing asymmetric built-in electric fields. Spatially resolved photocurrent measurements reveal that selective utilization of these built-in electric fields decouples the transport dynamics of dark and photogenerated carriers. Such decoupling allows for independent modulation of the dark current and photocurrent, enabling the concurrent realization of the ultralow dark current and high photocurrent. Moreover, gate-voltage modulation enhances the photoresponse by ~245%, yielding a detectivity of 1.98 × 10¹² Jones over the 532–940 nm range. Imaging and optical communication further verify the device’s practical potential. These results provide a viable route toward high-sensitivity and electrically reconfigurable broadband photodetectors. Full article

Accurate identification of fire smoke is a key link in realizing early fire prevention and control. Traditional intelligent video and image processing technologies are significantly restricted by environmental factors, with weak anti-interference capabilities and limitations in distinguishing fire smoke, leading to a high false alarm rate of fires. To address this problem, this paper proposes an unconventional visual field smoke detection method based on image enhancement. The method innovatively improves the Retinex algorithm by integrating improved guided filtering, adaptive brightness correction, and CLAHE-WWGIF joint processing, which realizes targeted optimization for the unique interference factors of under-construction nuclear power plants such as water mist, low illumination, and equipment occlusion. First, an improved Retinex algorithm is used to process the image to improve the image brightness and contrast, retain edge details while avoiding halo artifacts, reduce the impact of noise, and optimize visual features. Then, the sample data set is integrated, and the YOLOv11 target detection algorithm is used to achieve accurate identification and positioning of smoke targets. Experimental data shows that the fire identification method achieves an accuracy rate of 93.6% and 92.3% for fire smoke identification in interference-prone scenarios such as dark nights and water mist, respectively, and the response time to fire smoke is only 1.8 s and 2.1 s. In practical on-site applications at nuclear power plant construction sites, the method is integrated into an “edge computing + distributed deployment” hardware system, which realizes real-time smoke detection in core areas such as nuclear islands and conventional islands with a false alarm rate of less than 5% and a detection delay of ≤300 ms, meeting the ultra-strict safety monitoring requirements of nuclear power projects. Experiments show that this method can be effectively applied to smoke detection scenarios under unconventional visual fields, accurately identify smoke, provide reliable technical support for fire smoke identification under unconventional visual fields, significantly reduce the false alarm rate of fire detection, and provide technical support for the safety of under-construction nuclear power plants. Full article

While deep learning methods are increasingly applied in the field of DOA estimation, existing approaches generally feed the real and imaginary parts of the covariance matrix directly into neural networks without optimizing the input features, which prevents classical attention mechanisms from improving accuracy. This paper proposes a spatio-temporal fusion model named DACL-Net for DOA estimation. The spatial branch applies a two-dimensional Fourier transform (2D-FT) to the covariance matrix, causing angles to appear as peaks in the magnitude spectrum. This operation transforms the original covariance matrix into a dark image with bright spots, enabling the convolutional neural network (CNN) to focus on the bright-spot components via an attention module. Additionally, a spectrum attention mechanism (SAM) is introduced to enhance the extraction of temporal features in the time branch. The model learns simultaneously from two data branches and finally outputs DOA results through a linear layer. Simulation results demonstrate that DACL-Net outperforms existing algorithms in terms of accuracy, achieving an RMSE of 0.04° at an SNR of 0 dB. Full article

Optical components are extensively used in aerospace, microelectronic equipment, precision optical measurement, laser optics and other fields. Surface defects on optical components can significantly impact system performance, necessitating specialized detection methods. However, technical challenges persist in achieving high-resolution, high-precision and efficient optical surface defect detection. To address this, we propose an improved YOLOv8-based object recognition algorithm. By incorporating the BRA attention mechanism into YOLOv8’s backbone network, multi-scale feature maps are processed to enhance adaptability to complex scenarios. Simultaneously, replacing the feature fusion module with the Context-GuideFPN module enables contextual guidance and adaptive adjustments during multi-scale feature integration without excessive computational overhead. Experimental results on our high-quality microscopic dark-field image dataset demonstrate that the enhanced BACG-YOLOv8 achieves excellent performance in optical component defect detection. The optimized network accurately extracts defect details, particularly demonstrating refined edge feature extraction while effectively suppressing noise interference. This significantly reduces detection errors and improves defect extraction accuracy. Full article

The detection and characterization of exoplanets are central topics in astronomy, and high-contrast imaging techniques such nulling interferometry, coronagraphs, and extreme adaptive optics (ExAO) are key tools for the direct detection of exoplanets. This review synthesizes the pivotal role of these techniques in astronomical research and critically analyzes their role as key drivers of progress in the field. Nulling interferometry suppresses stellar light through the phase control of multiple telescopes, thereby enhancing the detection of faint planetary signals. This technology has evolved from the initial Bracewell concept to the LIFE (Large Interferometer For Exoplanets) technique, which will achieve a contrast ratio of 10⁻⁷ in the mid-infrared wavelength range in the future. Coronagraphs block starlight to create a “dark region” for direct observation of exoplanets. By leveraging innovative mask designs, theoretical contrast ratios of up to 4 × 10⁻⁹ can be achieved. ExAO systems achieve precise wavefront correction to optimize the high-contrast imaging performance and mitigate atmospheric disturbances. By leveraging wavefront sensing, thousand-element deformable mirrors, and real-time control algorithms, these systems suppress the turbulence correction residuals to 80 nm RMS, enabling ground-based telescopes to achieve a Strehl ratio exceeding 0.9. This work provides a comprehensive analysis of the underlying principles, prevailing challenges, and future application prospects of these technologies in astronomy. Full article

In the domain of intelligent inspection, the precise recognition of pointer meter readings is of paramount importance for monitoring equipment conditions. To address the challenges of insufficient robustness and diminished detection accuracy encountered in practical applications of existing methods for recognizing pointer meter readings based on object detection, we propose a novel approach that integrates YOLOv11-OBB rotating object detection with adaptive template matching techniques. Firstly, the YOLOv11 object detection algorithm is employed, incorporating a rotational bounding box (OBB) detection mechanism; This effectively enhances the feature extraction capabilities related to pointer rotation direction and dial center, thereby boosting detection robustness. Subsequently, an enhanced angle resolution algorithm is leveraged to develop a mapping model that establishes a relationship between pointer the deflection angle and the instrument range, facilitating precise reading calculation. Experimental findings demonstrate that the proposed method achieves a mean Average Precision (mAP) of 99.1% in a self-compiled pointer instrument dataset. The average relative error of readings is 0.41568%, with a maximum relative error of less than 1.1468%. Furthermore, the method exhibits robustness and reliability when handling low-quality meter images characterized by blur, darkness, overexposure, and tilt. The proposed approach provides a highly adaptable and reliable solution for pointer meter reading recognition in the intelligent industrial field, with significant practical value. Full article

Visual target detection under adverse weather conditions presents a fundamental challenge for autonomous driving, particularly in achieving all-weather operational capabilities. Unlike existing reviews that concentrate on individual technical domains such as image restoration or detection robustness, this review introduces an innovative “restoration–detection” collaborative framework. This paper systematically examines state-of-the-art methods for degraded image recovery and improvement of detection model robustness, encompassing from traditional, physically driven approaches as well as contemporary deep learning paradigms. A comprehensive overview and comparative analysis are provided to elucidate these advancements. Regarding the recovery of degraded images, traditional methods demonstrate advantages in interpretability within specific scenarios, such as those based on dark channel prior. In contrast, deep learning methods have achieved significant breakthroughs in modeling complex degradations and enhancing cross-domain generalization through a data-driven paradigm. In the field of enhancing detection robustness, traditional improvement techniques that utilize anisotropic filtering, alongside deep learning methods such as SSD, R-CNN, and the YOLO series, contribute to perceptual stability through feature optimization and end-to-end learning approaches, respectively. This paper summarizes 11 types of mainstream public datasets, examining their multimodal annotation system and addressing issues related to discrepancies. Furthermore, it provides an extensive evaluation of algorithm performance using PSNR, SSIM, mAP, among others. It has been identified that significant bottlenecks persist in dynamic weather coupling modeling, multimodal heterogeneous data fusion, and the efficiency of edge deployment. Future research should focus on establishing a physically guided hybrid learning architecture, developing techniques for dynamic and adaptive timing calibration, and designing a flexible multimodal fusion framework to overcome the limitations associated with complex environment perception. This paper serves as a systematic reference for both the theoretical development and practical implementation of automatic driving vision detection technology under severe weather conditions. Full article

Multimodal imaging is valuable because it can provide additional information beyond that obtained from a conventional bright-field (BF) image and can be implemented with a widely available device. In this paper, we investigate the implementation of speckle-based transmission (T) and dark-field (DF) imaging in a laboratory X-ray setup to confirm its usefulness for material analysis. Three methods for recovering T and DF images were applied to a sample composed of six materials: plastic, nylon, cardboard, cork, expanded polystyrene and foam with different absorption and scattering properties. Contrast-to-noise ratio (CNR) and linear attenuation, absorption and diffusion coefficients obtained from BF, T and DF images are studied for two object-to-detector distances (ODDs). Two analysis windows are evaluated to determine the impact of noise on the image contrast of T and DF images and the ability to retrieve material characteristics. The unified modulated pattern analysis method proves to be the most reliable among the three studied speckle-based methods. The results showed that the CNR of T and DF images increases with larger analysis windows, while linear absorption and diffusion coefficients remain constant. The CNR of T images decreases with increasing ODD due to noise, whereas the CNR of DF images exhibits more complex behaviour, due to the material-dependent reduction in DF signal with increasing ODD. The experimental results on the ODD dependence of T and DF signals are consistent with recently reported numerical simulation results of these signals. The absorption coefficients derived from T images are largely independent of the ODD and the speckle-based method used, making them a universal parameter for material discrimination. In contrast, the linear diffusion coefficients vary with the ODD, limiting their applicability to specific experimental configurations despite their notable advantages in distinguishing materials. These findings highlight that T and DF images obtained from a laboratory X-ray setup offer complementary insights, enhancing their value for material analysis. Full article

In order to solve the problem of imaging quality of industrial cameras for low-light and large dynamic scenes in many fields, such as smart city and target recognition, this study focuses on overcoming two core challenges: first, the loss of image details due to the significant difference in light distribution in complex scenes, and second, the coexistence of dark and light areas under the constraints of the limited dynamic range of a camera. To this end, we propose a low-light high-dynamic-range image enhancement method based on dynamic weights and pyramid fusion. In order to verify the effectiveness of the method, experimental data covering full-time scenes are acquired based on an image acquisition platform built in the laboratory, and a comprehensive evaluation system combining subjective visual assessment and objective indicators is constructed. The experimental results show that, in a multi-temporal fusion task, this study’s method performs well in multiple key indicators such as information entropy (EN), average gradient (AG), edge intensity (EI), and spatial frequency (SF), making it especially suitable for imaging in low-light and high-dynamic-range environments. Specifically in localized low-light high-dynamic-range regions, compared with the best-performing comparison method, the information entropy indexes of this study’s method are improved by 4.88% and 6.09%, which fully verifies its advantages in detail restoration. The research results provide a technical solution with all-day adaptive capability for low-cost and lightweight surveillance equipment, such as intelligent transportation systems and remote sensing security systems, which has broad application prospects. Full article

Background: Capillary tortuosity is a morphological variant of microcirculation. However, the mechanisms by which tortuous vessels meet metabolic requirements in health and disease remain unknown. We recently reported that capillary tortuosity score (CTS) is significantly higher in patients with septic shock than in steady-state individuals, and that CTS is significantly associated with alveolar-to-arterial oxygen (A-a O₂) gradient and oxygen debt in septic shock patients. Objective: We aimed to investigate the characteristics of the magnetic fields in the sublingual microcirculation of individuals with normal physiology and patients with septic shock. Methods: Systemic hemodynamics were recorded, and sublingual microcirculation was monitored using sidestream dark field (SDF+) imaging. The number of capillary red blood cells (N_RBC), the intensity of the magnetic field of a red blood cell (H_RBC), the intensity of the magnetic field of each capillary (H_CAP), and the intensity with which the magnetic field of a capillary acts on an RBC (F_CAP) were calculated. Results: Significant differences in macro- and microhemodynamic variables were observed between the two groups. Although N_RBC was significantly higher in individuals with steady-state physiology [87.4 (87.12) vs. 12.23 (6.9)], H_RBC was significantly stronger in patients with septic shock [5.9 × 10⁻¹⁶ (6.9 × 10⁻¹⁶) A m⁻¹ vs. 1.6 × 10⁻¹⁵ (1.4 × 10⁻¹⁵) A m⁻¹]. No significant difference was observed in H_CAP [2.16 × 10⁻¹⁴ (2.17 × 10⁻¹⁴) A m⁻¹ vs. 1.34 × 10⁻¹⁴ (1.23 × 10⁻¹⁴) A m⁻¹] and F_CAP [1.66 × 10⁻²⁴ (3.36 × 10⁻²⁴) A m⁻¹ vs. 6.44 × 10⁻²⁵ (1.1 × 10⁻²⁴) A m⁻¹] between the two groups. In patients with septic shock, H_RBC was associated with De Backer score (rho = −0.608) and venous–arterial carbon dioxide difference (rho = 0.569). In the same group, H_CAP was associated with convective oxygen flow (rho = 0.790) and oxygen extraction ratio (rho = −0.596). Also, F_CAP was significantly associated with base deficit (rho = 0.701), A-a O₂ gradient (rho = 0.658), and oxygen debt (rho = −0.769). Conclusions: Despite the microcirculatory impairment in patients with septic shock, H_RBC was significantly stronger in that group than in steady-state individuals. Also, H_CAP and F_CAP were comparable between the two groups. Tortuous vessels may function as biomagnetic coils that amplify RBC-induced magnetic fields, enhancing perfusion and oxygenation of adjacent tissues. Full article

Optical differential operations can directly extract edge information from images and have significant application potential in fields such as image processing and object recognition. In this work, we propose an optical spatial differentiator based on the birefringence effect of uniaxial crystals. The system comprises two orthogonal polarizers and a uniaxial crystal, offering advantages of structural simplicity, operational stability, low cost, and seamless compatibility with conventional optical systems. Experimental results demonstrate that the proposed differentiator achieves clear edge imaging for both amplitude and phase objects, while also enabling dark-field differential imaging of transparent biological cells, thereby substantially enhancing imaging quality and contrast. This efficient and robust design provides a promising solution for advancing optical differentiation techniques in applications ranging from data processing to biomedical imaging. Full article

To overcome chondrosarcoma’s (CHS) high chemo- and radioresistance, we used polyethylene glycol-encapsulated iron oxide nanoparticles (IONPs) for the controlled delivery of the chemotherapeutic doxorubicin (IONP_DOX) to amplify the cytotoxicity of proton radiation therapy. Human 2D CHS SW1353 cells were treated with protons (linear energy transfer (LET): 1.6 and 12.6 keV/µm) with and without IONP_DOX. Cell survival was assayed using a clonogenic test, and genotoxicity was tested through the formation of micronuclei (MN) and γH2AX foci, respectively. Morphology together with spectral fingerprints of nuclei were measured using enhanced dark-field microscopy (EDFM) assembled with a hyperspectral imaging (HI) module and an axial scanning fluorescence module, as well as scanning electron microscopy (SEM) coupled with energy-dispersive X-Ray spectroscopy (EDX). Cell survival was also determined in 3D SW3153 spheroids following treatment with low-LET protons with/without the IONP_DOX compound. IONP_DOX increased radiosensitivity following proton irradiation at both LETs in correlation with DNA damage expressed as MN or γH2AX. The IONP_DOX–low-LET proton combination caused a more lethal effect compared to IONP_DOX–high-LET protons. CHS cell biological alterations were reflected by the modifications in the hyperspectral images and spectral profiles, emphasizing new possible spectroscopic markers of cancer therapy effects. Our findings show that the proposed treatment combination has the potential to improve the management of CHS. Full article