Search Results (15,713)

Accurately tracking the spatial and temporal variations of water vapor is indispensable for weather forecasting and climate adaptation, yet remains challenging due to the sparse coverage and discontinuity of ground-based observations. Satellite remote sensing, particularly from geostationary satellites like Meteosat Third Generation Imager-1 (MTG-I1), offers continuous, high-resolution data. To the best of our knowledge, MTG-I1 is the first geostationary satellite equipped with a near-infrared (NIR) spectral band specifically designed for detecting water vapor. To address the lack of precipitable water vapor (PWV) data derived from the Flexible Combined Imager (FCI) onboard MTG-I1, a novel semi-empirical (SE) algorithm optimized for PWV retrieval is proposed. Validation against ground-based PWV measurements using an initial test set and a temporally independent test set yielded relative errors of no more than 0.10, indicating stable retrieval performance outside the model-development period. The FCI-derived PWV retrievals were also more accurate than the corresponding MODIS PWV data. Compared to the traditional radiative transfer model (RTM)-based retrieval method, the SE method shows greater adaptability to systematic differences between the observed and RTM-simulated FCI reflectance. After correcting for radiometric degradation, the RTM-based algorithm achieves a 41% reduction in absolute error and a 47% reduction in relative error, bringing its accuracy in line with the SE algorithm. Overall, the proposed SE algorithm demonstrates superior robustness and adaptability, and can provide more reliable remote sensing PWV data to support weather forecasting and climate research. Full article

This study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections. We examined the causal agents of widespread crop diseases, including: loose smut, head blight, fusarium head blight (FHB), stem rust, net blotch, spot blotch, common root rot. Analysing disease-specific spectral characteristics with machine learning (ML) algorithms revealed the most informative spectral ranges: the green region (~520–560 nm), the red chlorophyll absorption zone (~650–680 nm), and the red-edge region (~700 nm). These ranges accurately reflect alterations in the plant’s cellular structure and pigment complexes. Spectral data were processed using five ML algorithms. Random Forest (RF) proved to be the most effective for identifying and differentiating barley diseases, achieving an accuracy of up to 90.13% (MCC = 0.86). This superior performance stems from the ensemble method’s robustness to noise and its ability to extract critical features from high-dimensional hyperspectral data, particularly when distinguishing diseases with overlapping spectral signatures. Furthermore, this study highlights the potential of integrating UAV-based remote sensing to delineate reference zones, proximal hyperspectral imaging (HSI), and ML for robust plant health monitoring. This combined approach shows significant promise for early disease diagnostics, enabling site-specific treatments, curbing disease progression, and reducing pesticide application. Ultimately, these findings offer practical value for the agro-industrial sector in major grain-producing countries, especially in Central Asia, where agricultural advancement is a strategic priority for sustainable development and food security. Full article

Methane source-rate inversion from remote-sensing plume imagery is essential for emissions monitoring, but its accuracy is often limited by uncertainty in ancillary wind information. This study examines how physical knowledge can be integrated into a deep-learning inversion model when the available wind input is imperfect. Using a controlled large-eddy-simulation (LES) benchmark designed for EnMAP/PRISMA-style imaging-spectrometer methane quantification, we compare six models that span image-only regression, flexible wind conditioning, simplified hard integrated-mass-enhancement (IME) coupling, and soft physics-guided learning under clean inputs, deterministic wind bias, stochastic Gaussian wind noise, and source-rate-stratified tests. Under clean benchmark conditions, flexible wind conditioning provides the best scalar accuracy, with FiLM reaching a mean absolute percentage error (MAPE) of 6.19% and a root mean squared error (RMSE) of 1323.36, followed closely by Concat (MAPE 6.37%, RMSE 1325.69). The simplified hard-coupling model is sensitive to wind perturbations: DIN-hard rises from MAPE 8.44% under clean inputs to 31.39% and 26.89% under deterministic wind-bias multipliers α = 0.7 and α = 1.3, respectively, and becomes unstable under stronger Gaussian wind noise in the tested protocol. By contrast, DIN-soft-v2 remains competitive under clean conditions (MAPE 6.39%, RMSE 1360.94), follows smoother degradation under biased or noisy wind, and improves plume spatial diagnostics relative to DIN-soft (center-of-mass shift 3.92 versus 4.07 pixels; plume alignment degree 2.60 versus 2.72 degrees). The calibrated IME-style physical baseline reaches a clean MAPE 24.45%, indicating that the learning-based models substantially outperform this benchmark physical proxy. Within this LES-based benchmark and the tested wind-perturbation protocols, the results suggest that IME-inspired physical knowledge is more robustly incorporated as a calibratable soft prior than as the simplified hard log-additive forward coupling considered here; however, transfer to real satellite scenes still requires validation. Full article

Digital tools in aluminum alloy processing are computational, sensing-based, and data-driven methods used to understand, predict, monitor, optimize, and control how aluminum alloys are transformed into components. They support decisions across casting, deformation processing, heat treatment, welding, surface engineering, and additive manufacturing by linking processing conditions with geometry, microstructure, defects, properties, and service performance. In technical use, the term includes finite element method (FEM), computational fluid dynamics (CFD), CALculation of PHAse Diagrams (CALPHAD), microstructure models, machine-learning regressors, surrogate models, nondestructive digital inspection, image-analysis tools, and digital twins. These tools are most effective when they establish links among controllable processing variables, underlying metallurgical mechanisms, measurable quality indicators, and service-relevant performance outcomes. Full article

Pegmatite dikes are important prospecting indicators for rare-metal deposits, whereas traditional methods for pegmatite dike identification are constrained by the limited capability of human visual interpretation to capture information from remote sensing imagery, resulting in low identification accuracy and efficiency. In recent years, global research on semantic segmentation of different surface features and remote sensing-based mineral exploration using deep learning methods and high-resolution remote sensing imagery has made significant progress; however, studies on surface-exposed geological bodies such as pegmatite dikes remain highly insufficient. To address the key problem of efficiently identifying pegmatite dikes in remote sensing imagery, this study proposes an improved model based on UNet++, termed GAD-UNet++. In the field of remote sensing geology, this study constructed a pegmatite dike semantic segmentation dataset based on high-resolution RGB imagery by using 0.66 m RGB imagery for visual delineation and ZY1F hyperspectral data for spectral constraint and label refinement; on this basis, semantic segmentation of surface pegmatite dikes in the Yilanlike area of the South Tianshan Mountains, Xinjiang, was conducted using RGB remote sensing image patches as model input. Specifically, because pegmatite dikes are small targets characterized by slender structures, indistinct boundaries, and sparse regional distribution, this study introduced a lightweight feature extraction structure (GhostNetV2) and a long-range dependency attention module (DFC) at the encoder stage, and further incorporated the Coordinate Attention module (CA) to enhance spatial localization and boundary representation of the targets. Finally, focal cross-entropy loss and a deep supervision strategy were adopted to improve the accuracy of semantic information extraction for pegmatite dikes, as well as the training stability and segmentation accuracy under class-imbalance conditions. The results show that the proposed model achieved an mIoU of 93.11% and an F1-score of 94.95% on the test set. Compared with existing semantic segmentation models, the proposed model achieved superior performance in both identification accuracy and computational efficiency for pegmatite dikes. In addition, this study delineated 18 potential pegmatite dike enrichment zones in the Yilanlike area, providing technical support for remote sensing-based rare-metal prospecting and geological interpretation in the study area. Full article

Multimodal image registration is a fundamental yet challenging task, particularly in remote sensing scenarios involving cross-platform, multi-temporal, and cross-modal data. The primary difficulty arises from the coexistence of large-scale geometric distortions and complex local appearance variations across modalities, which makes it difficult for a single-stage model to achieve both global alignment and fine-grained correspondence simultaneously. To address this issue, we propose MMARNet, a task-driven coarse-to-fine registration framework that explicitly decomposes multimodal registration into global geometric alignment and local correspondence refinement. Instead of treating registration as a unified problem, the proposed framework sequentially resolves distinct sources of error, leading to improved robustness and accuracy under challenging conditions. In the first stage, MMARNet learns geometry-aware global alignment by identifying structurally reliable regions across modalities and estimating large-scale transformations, effectively reducing the initial misalignment and normalizing the geometric space. In the second stage, the model focuses on residual local discrepancies by learning context-enhanced feature representations, enabling robust keypoint-level matching even under severe modality differences and nonlinear distortions. The two stages are designed to work in a complementary manner, where global alignment significantly simplifies the subsequent local matching process. Extensive experiments on three challenging multimodal datasets demonstrate that MMARNet achieves superior performance in both accuracy and robustness compared to existing methods. The results validate the effectiveness of the proposed problem decomposition and highlight the advantage of the coarse-to-fine optimization strategy for multimodal remote sensing image registration. Full article

Acid phosphatase (ACP) is a clinically important enzyme whose early-stage detection is hindered by its extremely low abundance, nonspecific tissue distribution, and rapid loss of activity under conventional analytical conditions. Herein, we present a target-driven in situ nanozyme synthesis strategy that enables rapid and ultrasensitive point-of-care testing (POCT) of ACP. In this approach, ACP catalyzes the hydrolysis of L-ascorbic acid 2-phosphate sesquimagnesium (AAPS), producing ascorbic acid (AA). The generated AA partially reduces Fe³⁺ ions to Fe²⁺, thereby initiating alkaline co-precipitation and in situ formation of Fe₃O₄ nanoparticles. Polyvinylpyrrolidone (PVP) stabilizes the nanoparticles and preserves catalytic accessibility, while their intrinsic magnetism allows for efficient magnetic separation to eliminate matrix interference. The resulting Fe₃O₄@PVP nanozymes display pronounced peroxidase-like activity, catalyzing hydrogen-peroxide-mediated oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB). Quantitative readout can be achieved using either spectrophotometric analysis or smartphone imaging. The sensing platform achieves a detection limit of 0.021 U/L within 40 min and demonstrates excellent sensitivity, selectivity, and operational robustness. Successful validation in human serum confirms its clinical feasibility, while smartphone-based imaging enables portable and low-cost quantification suitable for decentralized diagnostics. Collectively, this work establishes a generalizable paradigm for target-triggered nanozyme generation aimed at detecting low-abundance and labile biomarkers. Full article

Dehazing of images is important for proper interpretation of optical images in remote sensing. However, current dehazing networks tend to have limited receptive field and texture information loss caused by conventional downsampling and complementary cross-domain information not being utilized in dehazing frameworks. In order to cope with these problems, we propose a Cross-domain Generative Prior-assisted Structure–Texture Adaptive Network for remote sensing image dehazing. It is a dual-stream encoder–decoder framework, which enhances the domain-specific information of RGB and generated prior, and then integrates them adaptively for haze-free reconstruction. In order to minimize information loss in downsampling, wavelet pooling is introduced to consider the frequency-aware structural and textural features. Additionally, a Structure–Texture Calibration Block is designed to simultaneously improve the local frequency textures and construct sparse long-range dependencies of structures, so as to achieve better restoration performance under spatially non-uniform haze. To appropriately fuse the various representations from RGB and generated prior images, a Prior-aware Gated Adaptive Fusion module is developed to balance the domain-specific features dynamically and keep the fine details at multi-level feature fusion. Finally, we utilize pixel-level contrastive learning to guide the latent space away from hazy distributions, thus enhancing the discriminability of the features. Extensive experiments on the three datasets, namely RSID, RICE-I and HRSD, demonstrate that CGSTA-Net can effectively restore images under varying haze conditions and significantly outperforms the latest dehazing methods in terms of visual quality and quantitative performance. Specifically, compared with the most effective competitive method, CGSTA-Net increased the PSNR by 22.9% on RSID, by 13.2% on RICE-I, and by 7.2% on HRSD. Full article

Image dehazing is a fundamental visual restoration task for improving visual perception under low-visibility weather conditions, especially in UAV-based remote sensing, traffic monitoring, and surveillance scenarios. Existing convolutional neural networks are effective in local feature extraction but remain limited in long-range dependency modeling, while Transformer-based methods improve global modeling at the cost of high computational complexity. To address these issues, this paper proposes an efficient image-dehazing framework termed FSSM, which integrates frequency-enhanced State Space Modeling with a hierarchical encoder–decoder architecture. Specifically, an FFT-based State Space Block (FFTSSB) is designed to reformulate state propagation as frequency-domain two-sided non-causal convolution, enabling efficient bidirectional global dependency modeling without explicit recursive scanning. Furthermore, a Frequency-Aware Discriminative Enhancement Block (FDEB) is introduced to enhance local textures, edges, and structural details through spatial gating and lightweight block-wise frequency modulation. Based on these two components, a Frequency-Aware State Interaction (FASI) block is constructed to progressively couple global state propagation and local frequency-aware enhancement. Experimental results on the HazyDet dataset demonstrate that FSSM achieves favorable restoration accuracy, structural consistency, and perceptual quality compared with representative dehazing methods. Ablation studies further validate the effectiveness of the proposed two-sided FFT-based state modeling, frequency-aware enhancement, and hierarchical multi-scale design. Full article

Introduction: Patients are increasingly aware of ways to manage their own health—especially regarding chronic diseases—along with the fundamental factors that should be present in well-organized and patient-oriented healthcare organizations. Due to the fact that the image of healthcare organizations depends on patients’ opinions, healthcare organizations are continuously improving and transforming their processes to increase patient satisfaction. This study aimed to analyze the relationship between patients’ opinions about the public hospitals in which they were treated and selected factors, including socio-demographic characteristics, previous hospital experiences, sources of information, and satisfaction with hospitalization in Poland. Methods: A cross-sectional survey was conducted among patients hospitalized in eight public hospitals in Wrocław. A self-developed questionnaire included two sections: (I) opinions about the hospital (11 items) and (II) expectations and satisfaction (12 items). Questionnaires were distributed in person. Data were analyzed using descriptive and inferential statistics, including correlation and chi-square tests. Results: Hospital image was shaped mainly by interpersonal factors, particularly staff kindness (82.9%), access to specialists (75.4%), and a sense of safety (54.4%). Women were more likely than men to seek information about hospitals before admission (47.6% vs. 39.3%; p = 0.021). A positive correlation was found between patient expectations and satisfaction with hospitalization (ρ = 0.425; p < 0.001). Media exposure played a minor role in shaping hospital image (22.1%), while personal recommendations and previous experience were the dominant sources of influence. Conclusion: Patients’ assessments of hospital image are determined primarily by relational and communication factors rather than infrastructural or technical aspects. Sociodemographic characteristics, such as gender and previous contact with the institution, may moderate these perceptions. The findings highlight the need to strengthen patient-centered care models, improve communication competencies among health professionals, and develop transparent institutional communication strategies. Full article

A remote sensing scene graph (RSSG) enables machines to interpret interactions among ground objects in remote sensing images and supports semantic reasoning and description, thus making it a fundamental technique in the field. However, most existing scene reasoning approaches cannot fully utilize multimodal information, resulting in limited performance when inferring spatial relationships among ground objects. To this end, we propose a Unified Visual-Semantic Triple Prompt Learning (UVSTPL) framework, which integrates visual features with matched geospatial object labels, leverages a prompt learning module for multimodal feature extraction, and employs a refined UVTransE model to predict spatial relationships. The core principle of UVSTPL is to enhance semantic feature extraction and improve relationship prediction performance via the collaborative fusion of visual and linguistic modalities. To strengthen the model’s ability to reason about the spatial relationships among ground objects in images, a novel Geo-RSSG dataset is constructed, which includes precise annotations of geographic entities, spatial relationships, and attributes. Extensive experiments demonstrate that the proposed UVSTPL method outperforms benchmark models on the spatial relationship prediction task. In comparison with the best baseline method, our approach improves prediction precision by 1.85%, mean precision by 8.49%, mean recall by 17.46%, and mean F1-score by 12.97%. This study offers valuable insights for advancing the understanding and cognitive capabilities of remote sensing scenes. Full article

Background/Objectives: Dental anxiety represents a major challenge in paediatric dentistry and is a frequent cause of non-cooperative behaviour during dental treatment. Nitrous oxide/oxygen inhalation conscious sedation is widely used to reduce anxiety in children, while audiovisual distraction is a non-pharmacological behavioural technique aimed at diverting attention from stressful stimuli. Evidence regarding the combined effect of these two approaches during dental treatment is still limited. Methods: This randomized crossover clinical trial included 25 paediatric patients aged 4–7 years with dental anxiety and previous failed attempts at conventional dental treatment. Each child underwent two dental treatment sessions under nitrous oxide/oxygen conscious sedation, one with and one without audiovisual distraction. Anxiety and behaviour were assessed using the Modified Venham Scale and the Facial Image Scale (FIS). Vital parameters were recorded before, during, and after sedation. Results: A significant reduction in heart rate over time was observed in both groups (p < 0.05). In children aged 4–5 years, the combined audiovisual distraction and conscious sedation approach was associated with significantly lower heart rate values compared to conscious sedation alone (p < 0.05). No significant differences were found between the two approaches for behavioural scores assessed by the Venham and FIS scales. Conclusions: Although behavioural scores did not differ significantly, audiovisual distraction contributed to greater physiological stability, particularly in terms of heart rate control. This no-pharmacological approach may complement the pharmacological effects of nitrous oxide sedation by enhancing the overall sense of relaxation and comfort during dental care. Full article

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article

Remote sensing referring segmentation aims to extract the exact region of an object in an aerial image based on a natural language description, but it remains challenging because remote sensing scenes cover large areas, many objects look similar, and the descriptions are often long and detailed. Existing attention-based models are computationally expensive on large images and may underuse fine-grained language cues, which can lead to inaccurate or incomplete masks. To address this, we present MSMamba, an efficient framework built on a state space model for stable long-range context modeling over large spatial grids. We further strengthen language grounding by identifying descriptive words in the expression and using them to guide visual features from coarse localization to boundary refinement. In addition, we design a scale-aware decoding strategy that fuses multi-scale features with adaptive gating to better handle severe size variation and thin structures. Experiments on four public benchmarks show that MSMamba consistently improves segmentation quality. On RefSegRS, MSMamba improves Pr@0.8 on the test set by 25.53% and increases mIoU by 6.65%. On RRSIS-HR, MSMamba improves Pr@0.8 by 9.09% and increases mIoU by 3.02%. These results suggest that combining a state space model with structured language guidance and scale-aware fusion is a practical alternative to attention-only designs for remote sensing referring segmentation. Full article