Search Results (18,999)

Landslide interpretation based on remote sensing data is pivotal for efficient emergency response and risk management. However, the scarcity of high-quality landslide data remains a major bottleneck for data-driven landslide analysis. To address this challenge, this study investigates the potential of multimodal foundation models for generating high-quality synthetic landslide and non-landslide remote sensing images. The proposed regional remote sensing image synthesis framework based on Stable Diffusion models and Low-Rank Adaptation enables more controllable and interpretable remote sensing data augmentation under data-scarce scenarios. Based on the publicly available Bijie landslide dataset, landslide and non-landslide remote sensing image–semantic annotation databases can be separately constructed and subsequently utilized to fine-tune text-to-image diffusion models. By conducting comparative experiments across three Stable Diffusion backbones, the performance of the generative models in both landslide and non-landslide scenarios is systematically and quantitatively evaluated. Experimental results demonstrate that LoRA fine-tuning can effectively transfer landslide-specific visual knowledge into diffusion models, enabling the generation of high-fidelity synthetic remote sensing images with texture and structure closely matching real samples. Compared with the StyleGAN2 baseline with a minimum FID of 67.47 in the recent literature, the proposed SDXL-LoRA model achieves superior generation quality with a minimum FID of 54.70. In addition, the study indicates that the optimal diffusion backbone depends on semantic complexity. Accordingly, a heterogeneous backbone strategy should be adopted when constructing balanced synthetic datasets for downstream applications. The training configurations employed in this study also provide a practical reference for related research. This study exploratorily applies multimodal generative foundation models to landslide-related remote sensing data augmentation and provides a flexible and transferable solution for regional geohazard studies. Full article

Reliable visual perception is a prerequisite for autonomous cable duct inspection, particularly for recognizing pipe-joint dislocations and obstruction-related hazards. Images acquired inside cable ducts are often affected by restricted viewpoints, uneven illumination, wall-texture interference, partial occlusion, and substantial variations in target geometry and scale. These factors increase the likelihood of missed targets, false alarms, and inaccurate bounding boxes. This study develops DPR-YOLOv9 from the YOLOv9c detector, where DPR represents deformable-strip feature extraction, position-aware attention, and regression optimization. In the backbone, a Deformable Strip Convolution Network (DSCN) adjusts its sampling pattern to better describe elongated boundaries, displaced joints, and irregular obstacle contours. CoordAttention is introduced into the multi-scale fusion path to retain directional coordinate cues and emphasize spatially relevant features. In addition, Inner-IoU modifies the regression constraint through auxiliary boxes, providing more effective optimization for small or partially occluded targets. Across three independent runs, DPR-YOLOv9 achieved mean Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.944, 0.933, 0.940, and 0.751, respectively, while maintaining an inference speed of 67.85 FPS. The results indicate that the proposed detector improves both recognition reliability and localization quality for robotic cable duct inspection. Full article

The eddy covariance (EC) technique is a key tool in environmental monitoring, enabling continuous and non-invasive measurement of carbon dioxide (CO₂) fluxes at the ecosystem–atmosphere interface. In environments characterized by high levels of airborne particulates, such as volcanic regions, the reliability of enclosed-path EC measurements can be compromised by frequent filter clogging, potentially affecting data continuity, and increasing maintenance requirements. This study investigates whether the chemical and mineralogical signatures of particulate matter accumulated on clogged Swagelok pre-Licor filters can be used to identify dominant particle sources and provide insights into filter clogging processes. A multi-analytical workflow combining scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS), portable Raman spectroscopy, and hyperspectral imaging (HSI) was applied to recovered filter residues. The combined approach provided complementary chemical, mineralogical, and morphological information, allowing discrimination among volcanogenic material (e.g., glass shards, crystals, and lithic fragments), aeolian lithogenic dust, including Saharan inputs, and biogenic particles such as plant fibers. The results revealed two dominant particulate groups, volcanogenic mineral phases and biogenic material, with a minor contribution from wind-transported lithogenic dust. Volcanogenic phases, enriched in Si, Al, and Fe, dominated the inorganic fraction, whereas O-, C-, and N-rich particles were mainly associated with local biogenic sources. No clear evidence of significant anthropogenic contributions was identified. These findings demonstrate that multi-analytical characterization of particles accumulated on EC pre-filters can provide qualitative source attribution and valuable information on the processes responsible for filter loading and clogging. By linking particle characteristics with meteorological and environmental conditions, this approach has the potential to support site-specific, predictive, and event-driven maintenance strategies, contributing to improved EC data quality and more efficient long-term monitoring in high-aerosol environments. Full article

Continuous maritime surveillance increasingly relies on optical remote sensing, yet small ships are difficult to detect in low-resolution imagery because limited spatial resolution, complex sea backgrounds, and sensor noise suppress the fine-grained cues on which detectors depend, while routine acquisition of high-resolution imagery is prohibitively costly. To address this problem, a hybrid framework (RGT-YOLOv5Det) is proposed that couples transformer-based super-resolution (SR) with a lightweight object detector so that fine spatial detail is restored before detection. Methodologically, a paired benchmark (Ship-HRRSI/Ship-LRRSI) was constructed from the public TGRS-HRRSD dataset by standardising images to 800 × 800 pixels and generating 200 × 200 pixel counterparts via 4× bicubic down-sampling; three SR models (RGT, HAT, and Real-ESRGAN) and four detectors (YOLOv5s, YOLOv8s, YOLOv10s, and Faster R-CNN-MobileNetV3-Large-FPN) were fine-tuned and compared under identical training settings, and the best-performing components were integrated into the proposed two-stage pipeline. In the results, RGT delivered the best reconstruction quality (PSNR 22.038 dB, SSIM 0.3502) with the fewest parameters (13.37 M), YOLOv5s proved the most resolution-robust detector, and the integrated RGT-YOLOv5Det achieved mAP@0.5 of 0.947 and mAP@0.5:0.95 of 0.768 on low-resolution imagery, exceeding the best standalone detector score on each metric by 0.033 and 0.089, respectively. It is concluded that restoring structural detail prior to detection offers an accurate and acquisition cost-efficient alternative to high-resolution imaging, providing a practical route to reliable small ship detection in degraded optical remote sensing imagery. Full article

Digital image steganography aims to imperceptibly embed secret information into a cover image to enable covert communication. This paper focuses on image-level imperceptibility and recovery quality, and proposes a cost-guided joint mask–perturbation optimization with attentive decoding for image steganography method (CMAD). In an end-to-end differentiable framework, CMAD jointly optimizes the embedding mask, perturbation magnitude, and decoding-network parameters, thereby improving recovery accuracy while preserving imperceptibility. During optimization, the proposed AniCost cost-map guidance mechanism computes pixel-level embedding costs through wavelet-based anisotropy analysis, and uses a probability-map guidance loss to directly encourage the mask to activate in complex-texture regions and deactivate in smooth regions. The channel-attention-based decoding network is fine-tuned for each image pair during optimization to adapt to the current pair. Experimental results show that the stego images generated by CMAD achieve PSNR values of 55–59 dB, while the recovered secret images achieve PSNR values of 35–40 dB. Full article

Hyperspectral images contain abundant spectral information and provide fine-grained spatial representations. However, their high dimensionality, severe spectral redundancy, subtle inter-class differences, mixed boundary regions, and limited labeled samples pose significant challenges to accurate classification. Convolutional neural networks (CNNs) have limited capability in modeling non-Euclidean structural relationships, whereas graph convolutional networks (GCNs) are susceptible to the quality of superpixel segmentation and noise propagation over graph structures. To address these issues in hyperspectral image classification, this paper proposes a Reliability-Aware Cross-Branch Contrastive Graph Convolutional Network (RACB-CGCN). The proposed method employs a dual-branch CNN–GCN architecture to extract pixel-level local spectral–spatial features and superpixel-level structural features, respectively. A superpixel reliability estimation and propagation control mechanism is introduced to assess node reliability based on the discrepancy between pixel-level features and superpixel-reconstructed features. This mechanism effectively suppresses the propagation of noisy information caused by impure superpixels and mixed boundary regions. Meanwhile, a cross-branch supervised contrastive learning strategy is developed to enhance semantic consistency between the CNN and GCN branches, thereby improving intra-class compactness and inter-class separability. In addition, a class-adaptive fusion module is designed to dynamically adjust the contributions of the two branches according to the feature characteristics of different land-cover classes. Experimental results demonstrate that the proposed method effectively exploits the complementary information between pixel-level fine-grained features and superpixel-level structural features, leading to improved classification accuracy and robustness in hyperspectral image classification. Full article

Rapid and nondestructive freshness evaluation of abalone is important for quality control during cold-chain distribution, yet conventional chemical and microbiological methods are destructive and labor-intensive. In this study, a smartphone image-based deep learning strategy was developed for abalone freshness classification under refrigerated storage. Abalone samples stored at 4 °C were imaged daily under natural light, and freshness labels were assigned according to total volatile basic nitrogen (TVB-N) measurements. A total of 1867 images were used to develop binary classification models, and a transfer learning-based ResNet50 model was further interpreted using Gradient-weighted Class Activation Mapping (Grad-CAM). TVB-N increased progressively during storage and exceeded the spoilage threshold on day 5 (15.63 ± 0.43 mg/100 g), which was used to define fresh (days 1–4) and spoiled (days 5–7) classes. Among the evaluated architectures, ResNet50 achieved the best overall performance, with a validation accuracy of 0.9611, precision of 0.9649, recall of 0.9091, and F1-score of 0.9362. On the test set, the model correctly classified 522 fresh and 220 spoiled images, yielding an overall accuracy of 96.11%. Grad-CAM visualization showed that the model mainly focused on the abalone body and marginal contour, indicating that predictions were driven by intrinsic appearance changes rather than background interference. These results demonstrate that smartphone imaging combined with deep learning provides a rapid, low-cost, and nondestructive approach for abalone freshness assessment and has potential for digital quality monitoring in shellfish cold chains. Full article

Deep learning for Alzheimer’s disease (AD) detection from structural magnetic resonance imaging (MRI) needs large, labelled datasets, yet many cohorts hold only a few hundred participants, for which conventional augmentation adds little anatomical diversity. In a two-stage pipeline, a variational autoencoder compressed 256 × 256 coronal slices to a 32 × 32 × 8 latent space, and a class-conditional latent diffusion model under classifier-free guidance generated AD and cognitively normal (CN) images using 295 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The pipeline reached a Kernel Inception Distance (KID) of 0.030 ± 0.002 and a bias-corrected Fréchet Inception Distance (FID_∞) of 43.82. A controlled ablation varying preprocessing alone improved KID by 0.0147 and precision by 0.069, both with 95% intervals excluding zero. FID did not separate the configurations. A ResNet-18 trained only on synthetic slices and tested on 44 held-out real participants (18 AD, 26 CN), each scored as the mean probability over twenty slices, reached an area under the curve of 0.779 ± 0.031 against 0.869 ± 0.027 for real data; the difference was not distinguishable at this sample size. No instance memorisation was found among 880 samples, and a size-matched control exposed a 27.7-percentage-point inflation in the standard memorisation metric. Preprocessing, therefore, measurably affects synthesis quality at the small-cohort scale, though not on every measure. Full article

Automated surface defect detection in hot-rolled steel is a prerequisite for real-time quality control, yet most deployed inspection systems operate in isolation from Industrial Internet of Things (IIoT) infrastructure. This paper reports a laboratory-scale proof of concept with two contributions. The first is a segmentation study on the Severstal dataset using a leakage-free, defect-stratified split of 1886 test images. Because a trivial all-background predictor already attains 96.66% pixel accuracy, performance is reported through Dice, IoU, precision, recall, and F1 with 95% confidence intervals. A compact from-scratch U-Net (0.49 M parameters) reaches a Dice of 0.416 at 38.6 ms per image, an ImageNet-pretrained DeepLabV3+ model reaches 0.677 at 46.3 ms and 37 times the parameters, and a classical Otsu baseline reaches 0.060, bracketing an explicit accuracy-versus-footprint design space rather than a single recommended model. The second contribution is architectural: a three-layer IIoT architecture whose messaging layer is empirically characterized on a Raspberry Pi broker over 158,500 messages. A factorial experiment isolates the transport configuration of the broker, rather than that of the publisher, as the determinant of end-to-end latency, yielding a seventeen-fold reduction. The layer sustains 1920 messages per second without loss, and a deliberate broker outage shows that MQTT delivery guarantees are semantic rather than temporal, motivating an application-level message-expiry policy. Embedded inference deployment is identified as the primary next step. Full article

Generative AI is increasingly integrated into organizational communication workflows, but organizations still have limited empirical evidence on how recipients evaluate AI-supported communication content when its source is not disclosed. This study examines how AI-generated organizational communication content becomes a decision alternative under hidden-source conditions and what this means for communication management. An exploratory mixed-method eye-tracking experiment was conducted with 20 participants, who evaluated six pairs of organizational communication stimuli, including text-based, image-based, and image-plus-text materials. Each pair contained one AI-generated and one human-created alternative, while the source remained concealed during the initial choice task. Data were collected through paired content-choice tasks, AOI-based eye-tracking with a Tobii Pro Spark 60 Hz screen-based eye tracker, an AI-identification task, and semi-structured post-experiment interviews. Within this six-pair exploratory stimulus set, AI-generated alternatives were selected in 66.7% of participant-by-task decisions, indicating that these specific AI-generated items were not automatically disadvantaged when their origin was hidden. Eye-tracking results showed broadly similar visual attention toward AI-generated and human-created content, while selected alternatives received higher attention than non-selected ones. Interviews revealed that participants evaluated content mainly through perceived quality, structure, professionalism, authenticity, and communicative suitability. The study contributes to research on AI-supported organizational communication by showing that responsible AI use requires human oversight, editorial control, and clear organizational guidelines. Full article

The economic impact of food fraud is difficult to quantify precisely, because fraud is structurally designed to evade detection; available estimates are indirect projections rather than direct forensic accounting and are commonly cited in the range of USD 10–15 billion annually. The integration of artificial intelligence (AI) with analytical instrumentation has generated a rapidly expanding body of research aimed at detecting adulteration, mislabeling, and substitution across food matrices. This systematic review examines the extent to which AI-assisted instrumental technologies contribute to food fraud prevention (as distinct from laboratory detection) and characterizes the structural factors that constrain real-world translation. A systematic search of the peer-reviewed literature published between 2021 and 2026 yielded 83 eligible records (80 primary studies and 3 review articles) after applying predefined inclusion criteria. Data were extracted into a structured seven-sheet workbook covering study characteristics, instrumental technologies, AI architectures, performance metrics, industrial-validation status, implementation evidence, and methodological quality. The corpus shows consistently high reported analytical accuracy under controlled laboratory conditions (median of extractable classification accuracies ≈ 99–100%; ≥95% in 86% of studies with an extractable value). At the same time, 68 of 83 studies (82%) reported no external validation, no study (0/83) achieved inter-laboratory validation, no study documented routine-monitoring application, and only one study reported testing in a genuine industrial environment. The most frequently featured platforms were NIR spectroscopy and electronic-nose arrays (each featuring in 30/83 studies, frequently in data-fusion combinations), followed by gas-chromatography-based systems (16/83) and hyperspectral imaging (13/83). Classical machine learning predominated (57/83 studies coded as classical ML, with a further 11 hybrid ML/DL designs and 12 deep-learning-only designs). A direct statistical comparison found no significant difference in reported accuracy between classical-ML and deep-learning studies (median 100% vs. 98.2%; Mann–Whitney U test, p = 0.16). A pre-specified test of the hypothesis that high reported accuracy is itself a marker of overfitting was not supported by the corpus: reported accuracy was not negatively associated with external-validation status (Fisher’s exact p = 0.51) or with methodological-quality score (Spearman ρ = 0.15, p = 0.23). Methodological quality was predominantly moderate (49/83 scored 3/5; 22 scored 2/5; 11 scored 4/5; one study scored 5/5), and 19/83 (23%) carried a high risk of bias. The review’s central observation—a measurable gap between demonstrated laboratory detection and evidenced real-world prevention—is well supported by the deployment, inter-laboratory, and routine-monitoring data. We deliberately separate this strongly evidenced conclusion from weaker inferences (e.g., the overfitting hypothesis) that the corpus cannot currently establish, and we outline a validation-driven, deployment-oriented research agenda. Full article

Artificial intelligence (AI) is embedded in healthcare through decision support, imaging, documentation, monitoring, digital twins, and smart-hospital infrastructures. This scoping review mapped technologies, healthcare contexts, socio-technical dimensions, governance mechanisms, and implementation conditions of AI-enabled healthcare systems. The review followed PRISMA-ScR. Scopus, Web of Science Core Collection, PubMed, and IEEE Xplore were searched on 1 July 2026 for English-language sources published from 2021 to 2026. All four authors participated in source selection; each record was assessed by two reviewers, and disagreements were resolved by consensus. Data were charted in matrices and synthesized descriptively and thematically. Of 2422 records, 426 duplicates were removed and 1996 were screened. Among 185 full-text reports, 124 were excluded, including 18 for insufficient methodological or empirical information, and 61 were included. Included sources then underwent a complementary seven-criterion cross-design appraisal scored from 1 to 3, without altering the final corpus. Technologies included machine learning, deep learning, decision support, explainable AI, natural language processing, large language models, interoperability frameworks, blockchain/IoMT, and digital twins. Challenges involved validation, data quality, interoperability, accountability, privacy, security, explainability, trust, bias, equity, and workforce readiness. Reported implementation facilitators included interoperable infrastructure, participatory design, lifecycle governance, continuous validation, and context-sensitive implementation. Full article

Lipid metabolites in maize kernels determine grain quality by influencing nutritional value, oxidative stability, and post-harvest deterioration, making their profiling essential for quality improvement and breeding. This study applies hyperspectral imaging (HSI) with a spectral range of 900–1700 nm to detect maize lipid metabolites. Six lipid metabolites—9-Octadecynoic acid (stearolic acid), pinolenic acid (Δ5,9,12 18:3), N-Acylethanolamine (16:0), N-Acylethanolamine (18:0), N-Acylethanolamine (18:1), and propionic acid—were selected due to their strong relevance to maize kernel quality and favorable spectral response. First, two-trace two-dimensional (2T2D) correlation spectroscopy with heterogeneous preprocessing is employed to capture both synchronous and asynchronous correlations across different preprocessing spectra. A convolutional autoencoder (CAE) was subsequently used to extract low-dimensional latent features from heterogeneous 2T2D-COS representations, followed by regression modeling using random forest (RF), support vector regression (SVR), gradient boosting (GB), and partial least squares regression (PLSR). A total of 82 maize seed varieties were employed for experimental validation. Compared with one-dimensional spectral, homogeneous preprocessing, and PCA-based feature extraction, the proposed approach provided improved predictive performance across the six lipid metabolites, with the optimal CAE-based models achieving R_P² values of 0.629–0.887, RMSEP values of 0.241–0.565, and RPD values of 1.656–2.069. Overall, this approach provides a rough screening solution for metabolite prediction in maize crop. Full article

Distributed Acoustic Sensing (DAS) has revolutionized ambient noise interferometry, yet the reliability of retrieved empirical Green’s functions (EGFs) remains highly sensitive to non-diffuse noise fields and anthropogenic transients. In complex or noisy environments, global metrics frequently misclassify signal convergence due to out-of-band environmental noise, scattered coda, and non-stationary directional transients. Using both 30 min and 4 h passive recordings, this study presents a physics-guided quality control framework for evaluating interferometric diagnostics. Specifically, this study employs the signal-to-trailing noise ratio (STRN), signal-to-precursory noise ratio (SPNR), and spectral signal-to-noise ratio (SSNR) within the surface-wave arrival window $t=x/v$. Global phase metrics remain heavily suppressed $(\overline{x}\approx 0.05)$ regardless of stacking duration, whereas the surface-windowed SPNR exhibits an extraordinary statistical shift (p < 0.001), reaching 0.870 ± 0.106 at 30 min and 0.967 ± 0.034 at 4 h. We implement one-to-one correspondence between surface-windowed indicator values and fundamental-mode Rayleigh wave dispersion sharpness. In severely noise-contaminated segments, unwindowed global metrics yield distorted dispersion ridges with severe energy leakage, but the surface-wave window results in an increase in the SPNR above 0.70, fully reconstructing continuous dispersion trajectories (250–500 m/s). Grounded in these results, we formalize a standardized four-step quality control workflow (from velocity windowing to metric calculation, automation, and data output) and outline tailored adaptation guidelines for urban, mountainous, and industrial DAS deployments. This framework provides an automated, physically sound protocol that eliminates manual selection, optimizes computational efficiency, and ensures reliable dispersion extraction for passive DAS imaging. Full article

Background: Malocclusion is a highly prevalent condition, affecting approximately 56% of the global population. Beyond its functional and physical characteristics, malocclusion has been associated with psychosocial outcomes, particularly body image concerns and perceived quality of life. The present study aimed to investigate the associations among Angle class, body image concerns, and oral health-related quality of life. Methods: A total of 136 adolescents aged 10 to 19 years (M = 15.49; SD = 2.64; 47.8% males and 52.2% females) were recruited from the “Gaetano Martino” University Hospital of the University of Messina. Following the clinical diagnosis and classification of malocclusion according to Angle’s classification (Class I, Class II, and Class III), participants underwent a psychodiagnostic assessment comprising a sociodemographic questionnaire, the Italian version of the Body Image Concern Inventory (I-BICI), and the Oral Health Impact Profile (OHIP-14). Results: Greater body image concerns were significantly associated with poorer oral health-related quality of life. Female participants reported significantly higher levels of body image concerns than males, whereas no significant sex differences emerged in OHIP-14 scores. Generalized linear models further showed that Angle class was significantly associated with body image concerns and oral health-related quality of life after controlling for age and sex. Conclusions: The findings indicate that Angle class is associated with body image concerns and oral health-related quality of life in adolescents with malocclusion. These results highlight the relevance of considering body image concerns and patient-reported oral health-related quality of life alongside conventional orthodontic assessment, supporting a more comprehensive, patient-centered approach to orthodontic care. Full article