Search Results (1,536)

Underwater image enhancement is crucial for fields like marine exploration, underwater photography, and environmental monitoring, as underwater images often suffer from reduced visibility, color distortion, and contrast loss due to light absorption and scattering. Despite recent progress, existing methods struggle to generalize across diverse underwater conditions, such as varying turbidity levels and lighting. This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. Extensive experiments on two benchmark datasets, UIEB and EUVP, demonstrate the superior performance of our method. On UIEB, our model achieves a PSNR of 22.91, SSIM of 0.9020, and CCF of 37.93—surpassing prior methods such as URSCT-SESR and PhISH-Net. On EUVP, it attains a PSNR of 26.12 and PCQI of 1.1203, outperforming several state-of-the-art baselines in both visual fidelity and perceptual quality. These results validate the effectiveness and robustness of our approach under complex underwater degradation, offering a reliable solution for real-world underwater image enhancement tasks. Full article

Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to MS. This study explores the potential of Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging to uncover vascular morphological features that may serve as MS-specific biomarkers. Using an age-matched, subject-wise stratified k-fold cross-validation approach, a deep learning model originally designed for color fundus images was adapted to segment optic disc, optic cup, and retinal vessels in IR-SLO images, achieving Dice coefficients of 91%, 94.5%, and 97%, respectively. This process included tailored pre- and post-processing steps to optimize segmentation accuracy. Subsequently, clinically relevant features were extracted. Statistical analyses followed by SHapley Additive exPlanations (SHAP) identified vessel fractal dimension, vessel density in zones B and C (circular regions extending 0.5–1 and 0.5–2 optic disc diameters from the optic disc margin, respectively), along with vessel intensity and width, as key differentiators between MS patients and healthy controls. These findings suggest that IR-SLO can non-invasively detect retinal vascular biomarkers that may serve as additional or alternative diagnostic markers for MS diagnosis, complementing current invasive procedures. Full article

Rockfill particle gradation significantly influences mechanical performance in earth–rockfill dam construction, yet on-site screening is often time-consuming, labor-intensive, and structurally invasive. This study proposes a rapid and non-destructive detection method using mobile-based photography and an end-to-end image segmentation approach. An enhanced YOLOv8-seg model with an integrated dual-attention mechanism was pre-trained on laboratory images to accurately segment densely stacked particles. Transfer learning was then employed to retrain the model using a limited number of on-site images, achieving high segmentation accuracy. The proposed model attains a mAP50 of 97.8% (base dataset) and 96.1% (on-site dataset), enabling precise segmentation of adhered and overlapped particles with various sizes. A Minimum Area Rectangle algorithm was introduced to compute the gradation, closely matching the results from manual screening. This method significantly contributes to the automation of construction workflows, cutting labor costs, minimizing structural disruption, and ensuring reliable measurement quality in earth–rockfill dam projects. Full article

The study of historical monuments within both architectural and literary frameworks reveals a dynamic interplay between scientific observation and artistic interpretation—a vital characteristic of travel writing/the travelogue. This approach, exemplified by British traveler and writer Henry Finnis Blosse Lynch (1862–1913), reflects how factual detail and creative representation are seamlessly integrated in depictions of sites, landscapes, and cultural scenes. This case study highlights Lynch as a pioneering explorer who authored the first comprehensive volume on Armenian architecture and as a writer who vividly portrayed Armenian monuments through both verbal description and photographic imagery, becoming the first traveler to document such sites using photography. Additionally, this paper emphasizes the significance of Lynch’s detailed accounts of architectural monuments, churches, monasteries, cities, villages, populations, religious communities, and educational institutions in vivid language. The careful study of his work can contribute meaningfully to the investigation of the travelogue as a literary genre and to the preservation and protection of the architectural heritage of historical and contemporary Armenia, particularly in regions facing cultural or political threats. Full article

Target detection is essential in intelligent transportation and autonomous control of unmanned aerial vehicles (UAVs), with single-stage detection algorithms used widely due to their speed. However, these algorithms face limitations in detecting small targets, especially in aerial photography from unmanned aerial vehicles (UAVs), where small targets are often occluded, multi-scale semantic information is easily lost, and there is a trade-off between real-time processing and computational resources. Existing algorithms struggle to effectively extract multi-dimensional features and deep semantic information from images and to balance detection accuracy with model complexity. To address these limitations, we developed FFG-YOLO, a lightweight small-target detection method for UAVs based on YOLOv8. FFG-YOLO incorporates three modules: a feature enhancement block (FEB), a feature concat block (FCB), and a global context awareness block (GCAB). These modules strengthen feature extraction from small targets, resolve semantic bias in multi-scale feature fusion, and help differentiate small targets from complex backgrounds. We also improved the positioning accuracy of small targets using the Wasserstein distance loss function. Experiments showed that FFG-YOLO outperformed other algorithms, including YOLOv8n, in small-target detection due to its lightweight nature, meeting the stringent real-time performance and deployment requirements of UAVs. Full article

Residential green space is an important component of urban green space and one of the major indicators for evaluating the quality of a residential community. Traditional indicators such as the green space ratio only consider the relationship between green space area and total area of the residential community while ignoring the difference in the amount of green space enjoyed by household residents in high-rise and low-rise buildings. Therefore, it is meaningful to estimate household green space and its spatial distribution in residential communities. However, there are frequent difficulties in obtaining specific green space area and household number through ground surveys or consulting with property management units. In this study, taking a composite residential community in Chongqing, China, as the study site, we first employed a five-lens drone to capture its oblique RGB images and generated the DOM (Digital Orthophoto Map). Subsequently, the green space area and distribution in the entire residential community were extracted from the DOM using VDVI (Visible Difference Vegetation Index). The YOLACT (You Only Look At Coefficients) instance segmentation model was used to recognize balconies from the facade images of high-rise buildings to determine their household numbers. Finally, the average green space per household in the entire residential community was calculated to be 67.82 m², and those in the high-rise and low-rise building zones were 51.28 m² and 300 m², respectively. Compared with the green space ratios of 65.5% and 50%, household green space more truly reflected the actual green space occupation in high- and low-rise building zones. Full article

Gypsum (CaSO₄·2H₂O) crystallization underpins numerous industrial processes, yet its response to chemical admixtures remains incompletely understood. This study investigates diffusion-controlled crystal growth in a coaxial test tube system to evaluate how three Sika^® ViscoCrete^® superplasticizers—430P, 111P, and 120P—affect nucleation, growth kinetics, morphology, and thermal behavior. The superplasticizers, selected for their surface-active properties, were hypothesized to influence crystallization via interfacial interactions. Ion diffusion was maintained quasi-steadily for 12 weeks, with crystal evolution tracked weekly by macro-photography; scanning electron microscopy and thermogravimetric/differential scanning were performed at the final stage. All admixtures delayed nucleation in a concentration-dependent manner. Lower dosages (0.5–1.0 wt%) yielded platy-to-prismatic morphologies and higher dehydration enthalpies, indicating more ordered lattice formation. In contrast, higher dosages (1.5–2.0 wt%) produced denser, irregular crystals and shifted dehydration to lower temperatures, suggesting structural defects or increased hydration. Among the additives, 120P showed the strongest inhibitory effect, while 111P at 0.5 wt% resulted in the most uniform crystals. These results demonstrate that ViscoCrete^® superplasticizers can modulate gypsum crystallization and thermal properties. Full article

Vitamin A deficiency (VAD) remains a significant cause of preventable blindness worldwide, with ocular surface changes representing early manifestations that require prompt recognition and treatment. Conventional examination methods are capable of detecting advanced changes; however, subtle conjunctival abnormalities may be overlooked, potentially delaying the administration of appropriate interventions. We herein present the case of a 5-year-old Japanese boy with severe VAD due to selective eating patterns. This case demonstrates the utility of infrared photography as a novel diagnostic approach for detecting and monitoring conjunctival surface abnormalities. The patient exhibited symptoms including corneal ulcers, night blindness, and reduced visual acuity. Furthermore, blood tests revealed undetectable levels of vitamin A (5 IU/dL), despite relatively normal physical growth parameters. Conventional slit-lamp examination revealed characteristic sandpaper-like conjunctival changes. However, infrared photography (700–900 nm wavelength) revealed distinct abnormal patterns of conjunctival surface folds and keratinization that were not fully appreciated on a routine examination. Following high-dose vitamin A supplementation (4000 IU/day), complete resolution of ocular abnormalities was achieved within 2 months, with infrared imaging objectively documenting treatment response and normalization of conjunctival surface patterns. This case underscores the potential for severe VAD in developed countries, particularly in the context of dietary restrictions, thereby underscoring the significance of a comprehensive dietary history and a meticulous ocular examination. Infrared photography provides a number of advantages, including the capacity for non-invasive assessment, enhanced visualization of subtle changes, objective monitoring of treatment response, and cost-effectiveness due to the use of readily available equipment. This technique represents an underutilized diagnostic modality with particular promise for screening programs and clinical monitoring of VAD-related ocular manifestations, potentially preventing irreversible visual loss through early detection and intervention. Full article

This article examines the application of generative artificial intelligence (GenAI) in visualizing meat products and evaluates its potential for use in the food industry. The study compares AI-generated images with conventional photographs in terms of professional accuracy and visual appeal. In a cited preliminary study, images of ten selected meat dishes were generated and evaluated by food technology professionals through a survey focused on realism and technical adequacy. Following this, comparable photographs were taken, and a second survey gathered feedback from the public on the appeal of both image types. Results revealed that while AI-generated images often lacked accuracy in texture, color, and structure, particularly for complex meat products, they were generally rated as more visually appealing by the public. This indicates that although current GenAI tools are not yet suitable for precise professional representation of meat products, they show strong potential for use in marketing and promotional content, where aesthetic appeal may outweigh technical accuracy. The findings suggest that with further development, AI-generated visuals could become more viable for professional applications in the food industry. In such cases, using accurate photographic references remains essential to ensure credibility and realism in food-related visual communication. Full article

UAVs are essential for forest fire detection due to vast forest areas and inaccessibility of high-risk zones, enabling rapid long-range inspection and detailed close-range surveillance. However, aerial photography faces challenges like multi-scale target recognition and complex scenario adaptation (e.g., deformation, occlusion, lighting variations). RGB-Thermal fusion methods integrate visible-light texture and thermal infrared temperature features effectively, but current approaches are constrained by limited datasets and insufficient exploitation of cross-modal complementary information, ignoring cross-level feature interaction. A time-synchronized multi-scene, multi-angle aerial RGB-Thermal dataset (RGBT-3M) with “Smoke–Fire–Person” annotations and modal alignment via the M-RIFT method was constructed as a way to address the problem of data scarcity in wildfire scenarios. Finally, we propose a CP-YOLOv11-MF fusion detection model based on the advanced YOLOv11 framework, which can learn heterogeneous features complementary to each modality in a progressive manner. Experimental validation proves the superiority of our method, with a precision of 92.5%, a recall of 93.5%, a mAP50 of 96.3%, and a mAP50-95 of 62.9%. The model’s RGB-Thermal fusion capability enhances early fire detection, offering a benchmark dataset and methodological advancement for intelligent forest conservation, with implications for AI-driven ecological protection. Full article

Background: Connubial melanoma, the occurrence of melanoma in non-consanguineous spouses, is rarely described in the literature. This study aimed to evaluate the prevalence of shared risk factors, preventive behaviors, and the influence of couple dynamics on the early diagnosis of cutaneous melanoma (CM). Methods: We conducted a retrospective observational study at the San Gallicano Dermatological Institute IRCCS, Rome, enrolling 52 heterosexual couples diagnosed with CM between 2010 and 2023. Clinical and anamnestic data, including phototype, history of sun exposure, use of tanning devices, and reason for dermatological evaluation, were collected. Dermatological assessments included dermoscopy, total body photography, and histological examination of excised lesions. Statistical analyses were performed using chi-square and Student’s t-tests. Results: Women reported significantly higher use of artificial ultraviolet sources (51.9% vs. 19.2%, p < 0.001) and more frequent histories of sunburn. Phototype II was associated with higher use of tanning devices and a greater prevalence of sunburns. Although the CM stage did not significantly differ between sexes, husbands exhibited a greater Breslow thickness. Melanoma localization differed by sex, with lower limbs more often affected in women and the trunk in men (p < 0.001). In 86.5% of cases, wives initiated their husband’s dermatological evaluation, leading to earlier diagnosis. Conclusions: Despite shared environmental exposures, men and women differ in preventive behaviors and risk profiles. Women play a crucial role in promoting early detection among couples. Couple-based preventive strategies may be instrumental in improving early melanoma diagnosis and outcomes. Full article

Background: Wound healing and scar management remain significant challenges in dermatology and aesthetic medicine. Recent advances in regenerative medicine have introduced plant-derived exosome-like nanoparticles (PDENs) as potential therapeutic agents due to their bioactive properties. This study examines the clinical application of rose stem cell exosomes (RSCEs) in combination with established treatments for managing different types of scars. Methods: A case series of four patients with different scar etiologies (dog bite, hot oil burn, forehead trauma, and facial laser treatment complications) was treated with RSCEs in combination with microneedling (Dermapen 4.0, 0.2–0.4 mm depth) and/or thulium laser therapy (Lutronic Ultra MD, 8–14 J), or as a standalone topical treatment. All cases underwent sequential treatments over periods ranging from two to four months, with comprehensive photographic documentation of the progression. The efficacy was assessed through clinical photography and objective evaluation using the modified Vancouver Scar Scale (mVSS) and the Patient and Observer Scar Assessment Scale (POSAS), along with assessment of scar appearance, texture, and coloration. Results: All cases demonstrated progressive improvement throughout the treatment course. The dog bite scar showed significant objective improvement, with a 71% reduction in modified Vancouver Scar Scale score (from 7/13 to 2/13) and a 61% improvement in Patient and Observer Scar Assessment Scale scores after four combined treatments. The forehead trauma case exhibited similar outcomes, with a 71% improvement in mVSS score and 55–57% improvement in POSAS scores. The hot oil burn case displayed the most dramatic improvement, with a 78% reduction in mVSS score and over 70% improvement in POSAS scores, resulting in near-complete resolution without visible scarring. The facial laser complication case showed a 75% reduction in mVSS score and ~70% improvement in POSAS scores using only topical exosome application without device-based treatments. Clinical improvements across all cases included reduction in elevation, improved texture, decreased erythema, and better integration with surrounding skin. No adverse effects were reported in any of the cases. Conclusions: This preliminary case series suggests that plant-derived exosome-like nanoparticles, specifically rose stem cell exosomes (RSCEs), may enhance scar treatment outcomes when combined with microneedling and laser therapy, or even as a standalone topical treatment. The documented objective improvements, measured by standardized scar assessment scales, along with clinical enhancements in scar appearance, texture, and coloration across different scar etiologies—dog bite, burn, traumatic injury, and iatrogenic laser damage—suggest that this approach may offer a valuable addition to the current armamentarium of scar management strategies. Notably, the successful treatment of laser-induced complications using only topical exosome application demonstrates the versatility and potential of this therapeutic modality. Full article

Background and Objectives: General-purpose multimodal large language models (LLMs) are increasingly used for medical image interpretation despite lacking clinical validation. This study evaluates the diagnostic reliability of ChatGPT-4o and Claude 2 in photographic assessment of adolescent idiopathic scoliosis (AIS) against radiological standards. This study examines two critical questions: whether families can derive reliable preliminary assessments from LLMs through analysis of clinical photographs and whether LLMs exhibit cognitive fidelity in their visuospatial reasoning capabilities for AIS assessment. Materials and Methods: A prospective diagnostic accuracy study (STARD-compliant) analyzed 97 adolescents (74 with AIS and 23 with postural asymmetry). Standardized clinical photographs (nine views/patient) were assessed by two LLMs and two orthopedic residents against reference radiological measurements. Primary outcomes included diagnostic accuracy (sensitivity/specificity), Cobb angle concordance (Lin’s CCC), inter-rater reliability (Cohen’s κ), and measurement agreement (Bland–Altman LoA). Results: The LLMs exhibited hazardous diagnostic inaccuracy: ChatGPT misclassified all non-AIS cases (specificity 0% [95% CI: 0.0–14.8]), while Claude 2 generated 78.3% false positives. Systematic measurement errors exceeded clinical tolerance: ChatGPT overestimated thoracic curves by +10.74° (LoA: −21.45° to +42.92°), exceeding tolerance by >800%. Both LLMs showed inverse biomechanical concordance in thoracolumbar curves (CCC ≤ −0.106). Inter-rater reliability fell below random chance (ChatGPT κ = −0.039). Universal proportional bias (slopes ≈ −1.0) caused severe curve underestimation (e.g., 10–15° error for 50° deformities). Human evaluators demonstrated superior bias control (0.3–2.8° vs. 2.6–10.7°) but suboptimal specificity (21.7–26.1%) and hazardous lumbar concordance (CCC: −0.123). Conclusions: General-purpose LLMs demonstrate clinically unacceptable inaccuracy in photographic AIS assessment, contraindicating clinical deployment. Catastrophic false positives, systematic measurement errors exceeding tolerance by 480–1074%, and inverse diagnostic concordance necessitate urgent regulatory safeguards under frameworks like the EU AI Act. Neither LLMs nor photographic human assessment achieve reliability thresholds for standalone screening, mandating domain-specific algorithm development and integration of 3D modalities. Full article

Low-altitude drones, which are unimpeded by traffic congestion or urban terrain, have become a critical asset in emergency rescue missions. To address the current lack of emergency rescue data, UAV aerial videos were collected to create an experimental dataset for action classification and localization annotation. A total of 5082 keyframes were labeled with 1–5 targets each, and 14,412 instances of data were prepared (including flight altitude and camera angles) for action classification and position annotation. To mitigate the challenges posed by high-resolution drone footage with excessive redundant information, we propose the SlowFast-Traffic (SF-T) framework, a spatio-temporal sequence-based algorithm for recognizing traffic accident rescue actions. For more efficient extraction of target–background correlation features, we introduce the Actor-Centric Relation Network (ACRN) module, which employs temporal max pooling to enhance the time-dimensional features of static backgrounds, significantly reducing redundancy-induced interference. Additionally, smaller ROI feature map outputs are adopted to boost computational speed. To tackle class imbalance in incident samples, we integrate a Class-Balanced Focal Loss (CB-Focal Loss) function, effectively resolving rare-action recognition in specific rescue scenarios. We replace the original Faster R-CNN with YOLOX-s to improve the target detection rate. On our proposed dataset, the SF-T model achieves a mean average precision (mAP) of 83.9%, which is 8.5% higher than that of the standard SlowFast architecture while maintaining a processing speed of 34.9 tasks/s. Both accuracy-related metrics and computational efficiency are substantially improved. The proposed method demonstrates strong robustness and real-time analysis capabilities for modern traffic rescue action recognition. Full article

As first-hand testimonies and accounts of the Holocaust fade, scholars and artists alike have struggled to depict and contextualize the genocide’s monumental violence. But depicting violence and its aftermath poses several problems, including the question of how to recall loss without artificially filling in or effacing the absence so central to its understanding. In essence, remembering the Holocaust is a paradox: the preservation of an absence. Marianne Hirsch’s influential concept of postmemory addresses this paradox and asks questions about memorial capacity in the twenty-first century. This essay considers Hirsch’s postmemory in the context of W.G. Sebald’s 2001 novel Austerlitz, which uses a combination of prose and photography to engage the difficulties inherent in memory work without access to eyewitnesses. Through the interaction of printed text and images, Austerlitz subtly references Lurianic mysticism’s concept of tikkun and Tree of Life (ilanot) diagrams. The result is a depiction of memory that is both process-based and embodies absence. My reading of Austerlitz traces a Jewish heritage within the work of a non-Jewish German author by attending to a tradition of mystical thought embedded in the novel. This situates Sebald’s fiction in a much longer Jewish history that stretches out on either end of the event of the Holocaust. Structurally, Sebald develops a tikkun-like process of (re)creation which relies on gathering material scraps of the past and imaginatively engaging with their absences in the present. Images, just as much as text, are central to this process. Reading Austerlitz in the context of Kabbalah reveals an intellectual and artistic link to a Jewish history that, while predating the Holocaust, nonetheless sheds light on post-Holocaust memories of loss. Full article