Search Results (9,297)

Foreign fiber (FF, plural: FFs) contamination has been demonstrated to have a substantial impact on the quality and profitability of cotton textiles. Machine vision technology, characterized by its non-contact approach and high efficiency, has emerged as the primary solution for detecting FFs in cotton. This paper commences with a precise definition and classification of FF and a concomitant analysis of the mechanisms of contamination. Subsequently, a systematic review of global research advancements in imaging technologies and the evolution of algorithms is conducted. This paper emphasizes the use of X-ray, ultraviolet fluorescence, line laser, polarized light, infrared imaging, and hyperspectral imaging techniques for FF detection. Through a comparative analysis, it reveals the applicable scope and effectiveness of various imaging schemes. Regarding the evolution of algorithms, this paper expounds on the technical development process from traditional image processing to machine learning (ML) and deep learning (DL). The study meticulously examines the strengths and weaknesses of each algorithmic stage. In conclusion, this paper synthesizes the prevailing technical challenges confronting machine vision detection of FFs in cotton and proffers recommendations for future research directions in this domain, emphasizing multi-technology integration, algorithm optimization, and hardware innovations. Full article

Background and Clinical Significance: Acute aortic dissection is the most common and most severe manifestation of acute aortic syndrome. An isolated dissecting aneurysm of the abdominal aorta is defined as a dissecting aneurysm distal to the diaphragm and is an extremely rare disease. Detection of an intimal flap between two lumens using different imaging methods is a definitive diagnostic sign of aortic dissection. A number of studies have validated ultrasound, including point-of-care ultrasound, as the standard initial imaging modality for the diagnosis of aortic dissection. Case Presentation: We present a 39-year-old patient who was sent to our institution under the suspicion of renal colic. The clinical findings revealed pale discoloration of the skin with sweating and abdominal pain. An emergency ultrasound showed an abdominal aortic aneurysm with an intimal flap, as well as free perirenal fluid on the left side. Multislice computed tomography aortography was then performed and the findings indicated rupture of a dissecting aneurysm of the abdominal aorta with a large retroperitoneal hematoma. The patient was then sent to a tertiary institution where he underwent emergency surgery and successfully recovered. Conclusions: Isolated abdominal aortic dissection is a rare condition with often non-specific clinical presentation, making imaging crucial for diagnosis. Ultrasound plays an important role as an initial imaging modality, as the detection of direct or indirect signs of dissection enables timely referral for CT aortography, confirmation of the diagnosis, and initiation of appropriate treatment. Full article

Background and Clinical Significance: Fibrolipoma is an uncommon histological variant of lipoma characterized by mature adipose tissue with a significant fibrous component. Intraoral lipomas are rare (only about 1–4% of all lipomas) and lipomas arising in the buccal fat pad (buccal space) are particularly uncommon. Case Presentation: A 47-year-old male presented with a painless, slowly enlarging swelling in the left cheek region. Physical examination revealed a soft, non-tender mass in the buccal space, causing mild bulging of the cheek. Contrast-enhanced computed tomography and magnetic resonance imaging demonstrated a well-circumscribed lesion within the left buccal fat pad suggestive of a lipoma. The tumor was excised entirely via an intraoral approach under general anesthesia. Histopathological examination showed lobules of mature adipocytes interspersed with dense fibrous connective septa consistent with a diagnosis of a fibrolipoma. The postoperative course was uneventful. Conclusions: This case highlights that fibrolipoma, while rare in the maxillofacial region, should be included in the differential diagnosis of buccal space tumors. Imaging studies can aid in identifying the fatty nature and extent of such lesions, but definitive diagnosis relies on histopathology. The buccal fat pad’s anatomy allows an intraoral surgical approach in appropriate cases, providing direct access and excellent cosmetic outcomes. Complete excision is curative in benign fibrolipomas, and careful surgical technique prevents injury to adjacent structures. Full article

We analyzed six years (2019–2025) of Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to quantify how turbidity and water temperature relate to algal blooms in Utah Lake. We generated satellite-derived estimates of chlorophyll-a (chl-a), turbidity, and surface temperature at 600 randomly distributed sample points. Using generalized least squares models, we found that temperature and turbidity explain only a small fraction of the variance in chl-a (temperature coefficients 0.02–0.03; turbidity coefficients −0.18–0.42), and the strength and sign of correlations vary by location. Despite weak linear correlations, we identified a strong nonlinear pattern: 94% of intense bloom events (chl-a > 87 µg/L) occurred when turbidity was below 120 Nephelometric Turbidity Units (NTU), indicating that blooms more often form under low-turbidity conditions. We also found that the first mild blooms of the season (chl-a > 34 µg/L) typically occurred five days after the largest short-term temperature increase (3–12 °C/day) at a given location, but only when blooms first appeared in April. These results suggest that Utah Lake blooms may be light-limited, with turbidity constraining algal growth that would otherwise occur in response to high nutrient levels, while temperature spikes influence early-season bloom initiation. Our findings have direct implications for monitoring and management strategies that target algal blooms on Utah Lake. Full article

Fluorinated colloidal nanosystems have attracted significant attention for their advantageous properties and potential applications in the biomedical field, especially in ¹⁹F magnetic resonance imaging. These nanosystems are known for their high specificity, excellent biocompatibility, and ease of functional modification. Furthermore, they offer unique advantages for functional surface coating due to their surface performance and chemical resistance. This paper discusses recent developments in fluorinated colloidal nanosystems, including applications in biological detection (such as enzymes, proteins, pH levels, ions, reducing environments, and reactive oxygen species) and surface coating (such as self-cleaning, self-healing, antibacterial properties, anti-fogging, antifouling, and oil–water separation). This article also highlights current challenges and provides suggestions for future research directions in the field of fluorinated colloidal nanosystems. Full article

In this study, a hybrid deep learning model that combines the VGG16 and ResNet101V2 architectures is proposed for image-based fire detection. In addition, a balanced drone guidance algorithm is developed to efficiently assign tasks to available UAVs. In the fire detection phase, the hybrid model created by combining the VGG16 and ResNet101V2 architectures has been optimized with Global Average Pooling and layer merging techniques to increase classification success. The DeepFire dataset was used throughout the training process, achieving an extremely high accuracy rate of 99.72% and 100% precision. After fire detection, a task assignment algorithm was developed to assign existing drones to fire points at minimum cost and with balanced load distribution. This algorithm performs task assignments using the Hungarian (Kuhn–Munkres) method and cost optimization, and is adapted to direct approximately equal numbers of drones to each fire when the number of fires is less than the number of drones. The developed system was tested in a Python-based simulation environment and evaluated using performance metrics such as total intervention time, energy consumption, and task balance. The results demonstrate that the proposed hybrid model provides highly accurate fire detection and that the task assignment system creates balanced and efficient intervention scenarios. Full article

Antonio Basoli’s one hundred Picturesque views of the city of Bologna (1824–1836) include 95 preparatory drawings, 16 of which were executed on oil-impregnated lightweight paper now showing advanced degradation. This study aims to investigate the materials and techniques used by Basoli’s workshop and to develop an evidence-based conservation approach for these fragile works. An integrated analytical methodology combining non-invasive hyperspectral imaging (HSI), Fiber Optics Reflectance Spectroscopy (FORS) and Ion Beam Analysis (IBA) with micro-invasive SEM-EDX and FTIR-ATR spectroscopy was applied on five drawings on lightweight impregnated paper to characterize both the paper supports and drawing media. Linseed oil containing lead-based drying agents was suggested to be the impregnating substance, while iron oxide (sanguine) over metalpoint (Cu, Sb, Pb) defined the graphic media. The detection of copper–lead residues suggests that Basoli employed a direct pressure transfer technique similar to James Watt’s copying machine. Conservation treatments resulted in significant pH stabilization (from 5.35 ± 0.20 to 6.45 ± 0.33) and reduced yellowing (ΔE* = 4.9 ± 1.8) while maintaining the paper’s translucency. The results elucidate the innovative practices of Basoli’s workshop and establish a reproducible analytical and conservation methodology applicable to the preservation of nineteenth-century graphic heritage. Full article

Background: Perceptual decision-making requires transforming sensory inputs into goal-directed actions under uncertainty. Neural oscillations in the theta band (3–7 Hz), particularly within frontal regions, have been implicated in cognitive control and decision confidence. However, whether changes in theta oscillations reflect greater effort during ambiguous decisions or more efficient control during clear conditions remains debated, and theta’s relationship to stimulus clarity is incompletely understood. Purpose: This study’s purpose was to examine how task difficulty modulates theta activity and how theta dynamics evolve across the decision-making process using two complementary analytical approaches. Methods: Electroencephalography (EEG) data were acquired from 26 healthy adults performing a face/house categorization task with images containing three levels of scrambled phase and Gaussian noise: clear (0%), moderate (40%), and high (55%). Theta dynamics were assessed from current source density (CSD) time courses of event-related potentials (ERPs) and single-trials. Statistical comparisons used Wilcoxon signed-rank tests with false discovery rate (FDR) correction for multiple comparisons. Results: Frontal theta power was greater for clear than noisy face stimuli (corrected p < 0.001), suggesting that theta activity reflects cognitive control effectiveness and decision confidence rather than processing difficulty. Connectivity decomposition revealed that frontoparietal theta coupling was modulated by stimulus clarity through both phase-locked (evoked: corrected p = 0.0085, dz = − 0.61) and ongoing (induced: corrected p = 0.049, dz = − 0.36) synchronization, with phase-locked coordination dominating the effect and showing opposite directionality to the induced components. Conclusions: Theta oscillations support perceptual decision-making through stimulus clarity modulation of both phase-locked and ongoing synchronization, with evoked component dominating. These findings underscore the importance of methodological choices in EEG-based connectivity research, as different analytical approaches capture different aspects of the same neural dynamics. The pattern of stronger theta activity for clear stimuli is consistent with neural processes related to decision confidence, though confidence was not measured behaviorally. Full article

Deep vision foundation models such as DINOv3 offer strong visual representation capacity, but their direct deployment in medical image segmentation remains difficult due to the limited availability of annotated clinical data and the computational cost of full fine-tuning. This study proposes an adaptation framework called StrDiSeg that integrates lightweight bottleneck adapters between selected transformer layers of DINOv3, enabling task-specific learning while preserving pretrained knowledge. An attention-enhanced U-Net decoder with multi-scale feature fusion further refines the representations. Experiments were performed on two publicly available ischemic stroke lesion segmentation datasets—AISD (Non Contrast CT) and ISLES22 (DWI). The proposed method achieved Dice scores of 0.516 on AISD and 0.824 on ISLES22, outperforming baseline models and demonstrating strong robustness across different clinical imaging modalities. These results indicate that adapter-based fine-tuning provides a practical and computationally efficient strategy for leveraging large pretrained vision models in medical image segmentation. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

This paper presents a superpixel segmentation algorithm that integrates anisotropic total variation regularization within a fuzzy clustering framework. While isotropic total variation is well-known for its edge-preserving properties, its non-adaptive nature often leads to over-regularization. In contrast, the anisotropic model formulates superpixel regularity in relation to image contours, thereby preventing the loss of image details in areas of high contour density during optimization. Compared to classical segmentation algorithms that employ non-adaptive regularization, the proposed content-adaptive approach enhances superpixel regularity while maintaining boundary adherence to image contours. Furthermore, to optimize the functional effectively, an alternating direction method of multipliers along with the enhanced Chambolle’s fast duality projection algorithm are employed. Competitive experiments against existing regular segmentation algorithms demonstrate that our proposed methodology achieves superior performance in terms of boundary recall, compactness, and shape regularity criteria, outperforming these methods by an average of at least 3%, 5%, and 3%, respectively. Furthermore, when compared with irregular segmentation algorithms, our approach achieves the best results in terms of compactness, contour density, and shape regularity criteria, with average improvements of at least 56%, 22%, and 45%, respectively. Full article

Multichannel audio is a sound field reproduction technology that uses multiple loudspeakers. Object-based audio is a playback method for multichannel audio that enables the construction of sound images at specified positions using coordinates within the playback space. However, the sound image positions must be manually specified by audio content creators, which increases the production workload, especially for works containing many sound images or feature films. We have previously proposed a method to reduce the workload of content creators by constructing sound images based on object positions in visual images. However, a significant challenge remains since depth localization of the sound image is not accurate enough. This paper aims to improve localization accuracy by changing the range of sound image movement along the depth direction. To confirm the localization accuracy of sound images constructed using the proposed method, we conducted a subjective evaluation experiment. The experiment identified the optimal movement range by presenting participants with visual images synchronized with sound images moving across varying spatial scales. Consequently, we were able to identify the range of sound image movement in the depth direction necessary for presenting sound images with high consistency with the visual images. Full article

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is one of the most common extra-articular manifestations of rheumatoid arthritis (RA) and a leading cause of mortality in RA patients. The diverse and nonspecific clinical presentations of RA-ILD make early diagnosis particularly challenging. In recent years, with a deeper understanding of the pathogenesis of RA-ILD and rapid advancements in medical imaging, artificial intelligence (AI) technologies, and biomarker research, notable progress has been achieved in the diagnostic approaches for RA-ILD. This review summarizes the latest research developments in the diagnosis of RA-ILD, with a focus on the clinical practice guidelines released in 2025. It discusses the application of high-resolution computed tomography (HRCT), the potential of AI in assisting HRCT-based diagnosis, and the discovery and validation of biomarkers. Furthermore, the review addresses current diagnostic challenges and explores future directions, providing clinicians and researchers with a cutting-edge perspective on RA-ILD diagnosis. Full article

Given the high cost of acquiring large-scale annotated datasets, few-shot object detection (FSOD) has emerged as an increasingly important research direction. However, existing FSOD methods face two critical challenges in remote sensing images (RSIs): (1) features of small targets within remote sensing images are incompletely represented due to extremely small-scale and cluttered backgrounds, which weakens discriminability and leads to significant detection degradation; (2) unified classification boundaries fail to handle the distinct confidence distributions between well-sampled base classes and sparsely sampled novel classes, leading to ineffective knowledge transfer. To address these issues, we propose TS-FSOD, a Transfer-Stable FSOD framework with two key innovations. First, the proposed detector integrates a Feature Enhancement Module (FEM) leveraging hierarchical attention mechanisms to alleviate small target feature attenuation, and an Adaptive Fusion Unit (AFU) utilizing spatial-channel selection to strengthen target feature representations while mitigating background interference. Second, Dynamic Temperature-scaling Learnable Classifier (DTLC) employs separate learnable temperature parameters for base and novel classes, combined with difficulty-aware weighting and dynamic adjustment, to adaptively calibrate decision boundaries for stable knowledge transfer. Experiments on DIOR and NWPU VHR-10 datasets show that TS-FSOD achieves competitive or superior performance compared to state-of-the-art methods, with improvements up to 4.30% mAP, particularly excelling in 3-shot and 5-shot scenarios. Full article

This in vitro study compared the antimicrobial and antibiofilm efficacy of four commercially available chlorhexidine (CHX)-based mouthwashes, with different nominal CHX concentrations, against clinically relevant postoperative oral pathogens, including Staphylococcus aureus, Streptococcus mutans, Escherichia coli, Pseudomonas aeruginosa, Candida albicans, and Candida auris. Antimicrobial potency was evaluated using MIC and CEMIC indices, while biofilm thickness reduction was quantified using 3D digital microscopy and custom image analysis software. Among the tested formulations, the excipient-enriched formulation exhibited the lowest MIC values and the most significant reduction in biofilm thickness, particularly against Gram-negative bacteria and Candida species. All mouthwashes achieved CEMIC < 0.1, confirming high theoretical applicability margins; however, CEMIC reflects potential clinical usefulness rather than clinical superiority. The findings demonstrate that the antimicrobial and antibiofilm activity of CHX rinses is formulation-dependent and cannot be predicted solely by CHX concentration. The influence of excipients is discussed as a possible contributing factor, but related mechanisms remain speculative and require direct validation in future studies. This work supports a formulation-driven, evidence-based approach to antiseptic comparison in postoperative dentistry, without assessing clinical wound-healing outcomes. Full article