Search Results (7,815)

Illegal drone intrusion near airport runways poses a critical threat to civil aviation safety, creating an urgent need for runway-side vision systems that can detect intruding UAVs early enough for safety warning and collision-risk mitigation. However, the development of such detectors is severely hindered by the scarcity of annotated real-world data in this high-security scenario. To address this bottleneck, we present SynthAirDrone, the first high-fidelity synthetic dataset for UAV intrusion detection in airport runway environments, together with an intelligent data generation framework integrating scene-aware placement and multi-criteria quality assessment. The proposed method uses sky-region segmentation to guide physically plausible drone placement, and combines perspective-aware scaling, Poisson image editing, and a four-dimensional quality scoring system—covering sky overlap, lighting consistency, size plausibility, and edge continuity—to improve visual plausibility and semantic consistency. The resulting dataset comprises 6500 high-quality images, all annotated in YOLO-compatible format. Using the lightweight YOLOv11n model, we show that models trained solely on SynthAirDrone exhibit non-trivial cross-domain transfer to Anti-UAV, while mixed training with limited real data provides the strongest real-world performance under the present setting. Ablation studies further confirm that a quality threshold of $\tau =0.6$ achieves the best trade-off between diversity and fidelity. Overall, this work delivers a reproducible and efficient synthetic data solution for UAV detector development in high-security, data-scarce airport-runway scenarios. Full article

Background: Achondroplasia, the most common genetic dwarfism caused by the FGFR3 mutation (autosomal dominant, 80% de novo), results in a disproportionately short stature. Thoracolumbar kyphosis (TLK), combined with characteristic spinal canal stenosis, increases the risk of symptomatic compression, yet the literature lacks clear thresholds for symptom onset or progressive deformity angles. Methods: A 16-year-old female with achondroplasia presented with rapidly progressive kyphosis despite conservative management (bracing and therapy). Over six months, she developed neurogenic claudication; bilateral leg pain; weakness; and paresthesia that worsened with standing/walking, which was relieved by flexion/sitting. Imaging demonstrated surgical-threshold kyphosis with progressive spinal misalignment. Her symptoms indicated compressive myeloradiculopathy from lumbar stenosis, critical given achondroplasia’s congenitally narrowed canal and heightened neurologic vulnerability. Results: Staged surgery planned: Posterior fusion T6-L4 with pedicle screws and then extensive decompression (laminectomy/foraminotomy T11-L3), L1 corpectomy with expandable titanium cage, and Ponte osteotomies. Intraoperative complications included a malpositioned left T10 screw breaching the anterior/lateral cortex near the aorta, requiring urgent revision. Postoperatively: Neurogenic bladder, wound leakage, and E. coli urinary tract infection (UTI) with fever (treated with IV antibiotics). After infection resolution, definitive surgery removed the malpositioned screw and completed decompression, corpectomy, cage placement, bone grafting, and osteotomies, successfully resolving neurological symptoms. However, 13 cm trunk lengthening caused severe functional impairment—disproportionately short arms prevented independent toileting and dressing. Left arm lengthening via external fixation restored partial function. At 2.5-year follow-up, there was solid fusion, no neurological deficits, and improved quality of life. Conclusions: Surgery addresses severe TLK, vertebral wedging, and neurogenic claudication in achondroplasia. Vertebral column resection effectively corrects TLK and neurological deficits but carries a high complication risk. This should be reserved for severe TLK with hypoplastic vertebrae, performed by experienced surgeons. Critically, correction magnitude must preserve limb–trunk proportions to prevent functional disability, as excessive lengthening may necessitate additional limb procedures for independence restoration. Full article

Background/Objectives: Pediatric intussusception, a condition where part of the intestine telescopes into an adjacent segment, predominantly affects children aged 6–18 months. Prompt diagnosis and management are crucial to prevent serious complications such as ischemia or necrosis. This systematic review aims to comprehensively evaluate and synthesize existing research on predictive and prognostic biomarkers associated with pediatric intussusception that can aid in early diagnosis, severity assessment, outcome prediction, and treatment. Methods: A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science using specific MeSH and free-text terms related to intussusception, biomarkers, and the pediatric population. The review followed PRISMA guidelines, with independent screening, data extraction, and quality assessment using the Joanna Briggs Institute critical appraisal tools. A total of 47 studies, mostly retrospective cohorts from diverse countries, with over 20,000 patients, were included. Results: The studies identified numerous biomarkers associated with disease severity, including hematological markers and indices (e.g., WBC counts and neutrophil-to-lymphocyte ratio), inflammatory markers (CRP and cytokines), biochemical markers (serum lactate, D-dimer, and electrolytes), and novel molecular markers (I-FABP, MCP-1, and transfer RNA fragments). Elevated inflammatory markers and derived ratios consistently predicted bowel necrosis, ischemia, and need for surgery. Biochemical markers like serum lactate and D-dimer correlated with ischemic severity. Emerging molecular biomarkers show promise for early, non-invasive risk stratification. However, heterogeneity in study designs, assay methods, and cutoff values currently limits immediate clinical application. Conclusions: Biomarker research offers valuable tools for improving pediatric intussusception management, with the potential to enhance early diagnosis and outcome prediction. While traditional markers are useful, novel molecular and protein biomarkers hold promise for more specific and rapid assessment. Validation through multicenter, prospective studies and standardized protocols is essential before routine implementation. Integrating biomarkers with clinical and imaging data could refine decision-making, ultimately reducing morbidity and improving prognosis in affected children. Full article

Polarimetric high-resolution range profiles (HRRPs) contain rich amplitude and phase information scattered from targets, making them essential for radar remote sensing applications. However, current HRRP imaging methods still face challenges in achieving precise full-polarization measurements. In addition, they are either affected by off-grid errors thus introducing spurious scattering centers (SCs), fail to utilize polarimetric priors from the channels, or encounter high computational complexity. Some of these issues limit the quality of polarimetric HRRPs, while others result in excessive computational load, hindering their application on orbital remote sensing platforms. This paper proposes a fast gridless polarimetric HRRP imaging method. First, we introduce the novel virtual full polarization sparse stepped-frequency waveforms (VFP-SSFW) to improve channel isolation, in which each pulse is transmitted with either horizontal (H) or vertical (V) polarization, selected uniformly at random. Then, we propose a polarimetric atomic norm minimization (P-ANM)-based imaging framework formulated within distributed compressed sensing (DCS), which fully exploits the joint sparsity across polarization channels while inherently eliminating off-grid errors. Additionally, we develop a fast algorithm based on alternating direction method of multipliers (ADMM) to enable efficient implementation. The proposed method can circumvent transmission channel crosstalk and can efficiently yield high-quality polarimetric HRRPs with co-registered SCs . The validity of the proposed method is demonstrated through simulated, electromagnetic, and measured experimental results. Full article

Background: This study investigates the impact of anatomically constrained preprocessing and deep learning architecture selection on benign versus malignant breast lesion classification in contrast-enhanced mammography (CEM), with the goal of improving robustness and clinical reliability across heterogeneous data sources. Methods: In this retrospective multicenter study, CEM images from 300 patients (314 lesions) were combined with 1003 publicly available CEM images, yielding a total of 1120 breast cases. Automatic breast segmentation was performed using the LIBRA framework to generate breast-mask images. Eleven deep learning models, including classical convolutional neural networks, attention-based networks, hybrid convolutional neural networks (CNNs), Transformer architectures, and mammography-specific models, were trained and evaluated using both original DICOM images and breast-mask inputs. Performance was assessed using accuracy, balanced accuracy, sensitivity, specificity, AUROC, and AUPRC on cross-validation and independent test sets. Hyperparameter optimization was conducted for the best-performing architecture. Results: Models trained on breast-mask images consistently outperformed those trained on original DICOM images across all architectures and metrics, with AUROC improvements ranging from +0.06 to +0.21. Among all models, ResNet50 trained on breast-mask images achieved the best performance (AUROC = 0.931; AUPRC = 0.933; balanced accuracy = 0.834), further improved after optimization (balanced accuracy = 0.886; sensitivity = 0.842; specificity = 0.930). Classical CNN architectures demonstrated performance comparable to or exceeding that of more complex hybrid CNN–Transformer models when anatomically focused preprocessing and rigorous optimization were applied. Conclusions: Anatomically constrained preprocessing through breast-mask segmentation substantially enhances deep learning performance and stability in CEM-based breast lesion classification. These findings indicate that input representation quality and training optimization are critical determinants of clinically relevant performance, often outweighing architectural complexity, and may support more reliable AI-assisted decision support in CEM workflows. Full article

As modern urban systems advance, video surveillance has become indispensable for ensuring high-quality urban development. Nighttime images acquired in urban monitoring scenarios are often degraded by haze and non-uniform illumination, resulting in reduced visibility, color distortion, and blurred structural boundaries. To address these issues, this paper proposes a nighttime image dehazing framework that combines mixed-norm variational atmospheric-light estimation with adaptive boundary-constrained transmission refinement. Specifically, an  L₂ − L_p mixed-norm regularization model is introduced to improve atmospheric-light estimation under complex nighttime illumination and suppress halo diffusion and color distortion around strong light sources. In addition, an adaptive boundary-constrained transmission refinement strategy with weighted soft-threshold shrinkage is developed to reduce residual artifacts while preserving structural edges. Experimental results on synthetic and real nighttime haze datasets demonstrate that the proposed method consistently outperforms representative state-of-the-art methods in both visual quality and quantitative metrics, showing superior robustness and restoration performance for nighttime urban monitoring applications. Full article

Odour recognition with low-cost gas sensors is challenging in open-air settings due to the non-specificity of the sensors and environmental variability. This can be mitigated by incorporating additional information into the classification process. This paper investigates odour-image multimodality in two case-studies of increasing complexity: banana ripening in open-air environment and strawberry ripening in a glasshouse environment. Data were collected using custom acquisition platforms equipped with cameras and MOX gas sensors operated with temperature modulation. For the visual modality, image classification (MobileNetV3) and object detection (YoloV5) models are trained. For the odour modality, established classical machine learning methods (Random Forest, XGBoost, SVM and Logistic Regression) and neural networks (1D-CNN, LSTM, MLP, and ELM) are employed. Each modality is analysed independently and together to critically assess scenarios in which combining modalities provides a clear advantage over using either modality alone. Results show that models trained on odour data achieve high accuracy in controlled environments but underperform in more dynamic open-air settings. Image-based models are sensitive to the image quality in all environments; however, they are more robust when deployed in different environments. Lastly, it is demonstrated that decision fusion consistently increases the accuracy, by as much as +12.36% in the banana ripening and +3.63% in the strawberry ripening scenario. Where decision fusion does not improve classification accuracy significantly, it is shown that the multimodal approach can still be leveraged to identify high-confidence predictions by selecting samples where both modalities agree on the label. Full article

Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MAR) utilizing a Cycle-Consistent Adversarial Network (CycleGAN) optimized for high-fidelity restoration. Unlike supervised methods that rely on unattainable voxel-aligned paired datasets, the proposed approach leverages an unpaired dataset of approximately 4000 images, curated from the public ToothFairy dataset. The architecture integrates U-Net-based generators and PatchGAN discriminators, specifically tuned to mitigate generative hallucinations and preserve morphological integrity. Quantitative benchmarking on a held-out test set demonstrates a 34.6% improvement in the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score, a substantial reduction in Fréchet Inception Distance (FID) from 207.03 to 157.04, and a superior Structural Similarity Index Measure (SSIM) of 0.9105. The framework achieves real-time efficiency with a 3.03 ms inference time per slice, effectively suppressing artifacts while preserving anatomical detail. Expert validation confirms high fidelity; however, to ensure reliability in extreme cases, the architecture is recommended as a clinical decision-support tool under human-in-the-loop oversight. By enhancing diagnostic clarity via a scalable software pipeline, this study provides a robust solution for high-fidelity dental implant imaging. Full article

Background: Androgen deprivation therapy (ADT) is widely used in the management of prostate cancer (PCa) and remains a cornerstone of treatment across multiple disease settings. Although ADT contributes substantially to disease control, it also induces significant adverse metabolic and body composition changes. These alterations include loss of lean mass, increased fat mass, and deterioration in muscle quality, together contributing to a clinical phenotype consistent with sarcopenic obesity (SO). Importantly, ADT-induced SO is characterized not only by reductions in skeletal muscle mass but also by impaired muscle quality, particularly the fatty infiltration of skeletal muscle, or myosteatosis, an underrecognized but defining feature of this syndrome. Methods: This narrative review examines current evidence regarding interventions aimed at mitigating sarcopenic obesity in men treated with ADT for prostate cancer, identifies key gaps in the literature, and proposes a mechanism-driven path forward for intervention development. Results: Several exercise- and nutrition-based interventions have been evaluated in men receiving ADT and demonstrate improvements in selected outcomes such as muscle strength, body composition, and metabolic parameters. However, most studies have been limited by small sample sizes, short intervention durations, and a focus on isolated intervention components. Importantly, muscle quality and intramuscular fat infiltration (myosteatosis), a central component of sarcopenic obesity, have rarely been incorporated as biomarkers or endpoints in intervention trials targeting men receiving ADT. Conclusions: Future interventions designed to mitigate SO and its associated metabolic abnormalities should evaluate comprehensive, bundled strategies initiated early during ADT and sustained long enough to capture clinically meaningful changes. Outcomes should include biomarkers of muscle mass, strength, and quality, including imaging-based measures of myosteatosis, along with metabolic syndrome markers, inflammatory mediators, functional outcomes, adherence, and quality of life. These changes should evaluate the correlation with underlying biological mechanisms such as NF-κB signaling and pro-inflammatory cytokines. Such data may inform future phase III trials and ultimately support clinical strategies to mitigate ADT-related sarcopenic obesity and its downstream cardiometabolic and oncologic consequences. Full article

Reliable developmental benchmarks are essential for synchronizing incubation and first-feeding decisions in striped bass (Morone saxatilis) hatcheries, yet existing references are incomplete, outdated, or difficult to apply across variable temperature regimes. We developed contemporary embryo and larval developmental staging guides for striped bass using digital imaging and degree day standardization and paired these guides with measurements of early larval mortality and endogenous energy depletion to provide practical context for hatchery management. Larvae were photographed from hatch through metamorphosis to document key morphological transitions, including yolk absorption, mouth formation, swim bladder inflation, fin differentiation, pigmentation, and diet-related developmental milestones. To place these stages in physiological and survival context, aquarium trials showed there was no clear density-dependent mortality across rearing densities of 1.1–6.8 larvae/mL within the first 72 h post-hatch. Yolk reserves were typically depleted by approximately 4–6 days post-hatch (dph), while lipid droplets persisted longer as secondary endogenous energy stores in unfed larvae through 15 dph. Together, these staging guides provide a transferable developmental framework from fertilization to metamorphosis that links external morphology to endogenous reserve depletion and first feeding, thus supporting standardized hatchery monitoring, improved feeding synchronization, and more consistent assessment of embryo and larval quality. Full article

While pre-trained deep learning models have significantly advanced image dehazing, their restoration performance often fluctuates substantially across varying haze densities, leading to inconsistent performance across diverse atmospheric conditions. To address this limitation, this study introduces a performance analysis approach based on Haze Image Clustering (HIC) to systematically evaluate the specialized strengths of various state-of-the-art models within specific haze-level intervals. Building upon these evaluations, we propose a heterogeneous modular framework equipped with a dynamic switching mechanism that adaptively activates the optimal pre-trained module for each detected haze level. Extensive experiments conducted on the OTS and ODF benchmark datasets demonstrate that while individual models exhibit regional performance drops, the proposed framework consistently maintains superior performance across all haze intensities. Quantitative results indicate that the proposed modular network achieves a significant PSNR improvement of up to 6.946 dB compared to DehazeFlow. Furthermore, regarding the no-reference Dehazing Quality Index (DHQI), our framework attains a top score of 68.448, surpassing the best individual baseline. These findings validate that the proposed strategy effectively enhances both restoration fidelity and visual naturalness without the need for additional training or fine-tuning, offering a robust and computationally efficient solution for real-world image dehazing. Full article

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

Ensuring the durability and safety of modern infrastructure critically depends on the quality and strength of concrete. The Ultrasonic Pulse Velocity (UPV) method is a widely used non-destructive testing technique for evaluating concrete properties; however, factors such as aggregate size distribution, compaction methods, and surface quality can significantly influence UPV results and their correlation with compressive strength. This study investigates the effects of different aggregate sizes and an innovative vibration-assisted compaction method—developed using piezoelectric (PZT) transducers—on the mechanical, ultrasonic, and surface properties of concrete. Four distinct aggregate size distributions were employed to produce sixteen concrete specimens with constant mix proportions. Unlike conventional low-frequency, high-power vibration practices, a high-frequency (40 kHz), low-power (120 W) vibration protocol was applied through PZT elements placed within the molds to enhance compaction and reduce entrapped air. Experimental results indicated that the heaviest specimen (7.13 kg) was the medium-aggregate sample compacted using tamping and rodding methods. The highest UPV value (4143 m/s) was obtained from the coarse-aggregate specimen subjected to three minutes of vibration. In contrast, the best compressive strength performance (22.73 MPa) was observed in the medium-aggregate specimen without any vibration treatment. The findings revealed that both aggregate size and advanced vibration techniques have significant effects on the mechanical properties, ultrasonic response, and surface quality of concrete. In addition, a proof-of-concept portable surface-finishing prototype consisting of a steel plate instrumented with multiple PZT transducers was developed, and preliminary trials qualitatively suggested improved surface leveling when applied in contact with the concrete surface. Surface roughness was quantified via image processing (Light Map 150 and Specular Map 150). The rough-area fraction decreased from ~29.8% in the untreated specimen to ~4.3% after ultrasonic application, indicating a marked improvement in surface leveling and overall surface quality. The results indicate that the applied PZT vibration protocol did not improve compressive strength; in several cases, particularly under prolonged excitation, a reduction in strength was observed. In contrast, a significant improvement in surface quality was achieved, with the rough-area fraction decreasing from approximately 29.8% to 4.3%. However, due to the limited number of specimens, the findings should be interpreted as preliminary. Overall, the method appears more promising as a surface enhancement technique rather than a direct alternative to conventional compaction methods. Full article

Background: Laryngeal cancer (LC), a common subtype of head and neck cancers (HNC), is most frequently represented by laryngeal squamous cell carcinoma (LSCC). Prognosis largely depends on early detection; however, traditional prognostic tools, including tumor-node-metastasis (TNM) staging, often show limited predictive accuracy. Artificial intelligence (AI), including machine learning (ML), natural language processing, and deep learning (DL), has emerged as a promising approach to improving cancer diagnosis, prognosis, and treatment planning by analyzing clinical data and medical imaging. Objective: This systematic review assesses the role of AI in prognosis, recurrence prediction, and treatment outcomes in LC. Methods: PubMed, MEDLINE, Scopus, Web of Science, IEEE Xplore, and ScienceDirect were searched up to January 2025. A total of 1062 records were identified; after title/abstract screening and full-text assessment, 29 studies were included. Eligible studies involved adult patients with LC and applied AI to diagnose, prognose, predict recurrence, or assess treatment outcomes using human datasets. Study quality and risk of bias were evaluated using the QUADAS-2 and QUIPS. Results: The 29 included studies were mostly retrospective, with sample sizes ranging from 10 to 63,000 patients. Most focused on LSCC, with a higher prevalence in males. The studies utilized various AI techniques, including deep learning models such as convolutional neural networks (CNNs) and DeepSurv, as well as ML algorithms like random survival forest, gradient boosting machines, random forest, k-nearest neighbors, naïve Bayes, and decision trees. AI models demonstrated strong prognostic performance, surpassing Cox regression and TNM staging in predicting survival and recurrence. Several studies reported outcomes related to treatment, such as chemotherapy response, occult lymph node metastasis, and the need for salvage surgery. Methodological quality varied, with biases related to patient selection and confounding factors. Conclusions: AI has the potential to improve prognosis estimation, recurrence prediction, and treatment outcome assessment in LC. However, although AI can be a helpful addition to clinical decision-making, more prospective studies, external validation, and standardized evaluation are necessary before these technologies can be confidently adopted in everyday clinical practice. Full article