Search Results (156)

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

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: 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

Micro-textures are crucial for enhancing surface performance in diverse applications, but traditional radial electrochemical micromachining (REMM) suffers from process complexity and workpiece damage. This study presents radial ultrasonic rolling electrochemical micromachining (RUREMM), an advanced technique integrating an ultrasonic field to improve electrolyte renewal, disrupt passivation layers, and optimize electrochemical reaction uniformity on SS304 surfaces. Aimed at overcoming challenges in precision machining, the research explores the synergistic effects of ultrasonic energy and flow field dynamics, offering novel insights for high-quality metal micromachining applications. The research establishes a mathematical model to analyze the interaction between the ultrasonic energy field and electrolytic machining and optimizes the flow field in the narrow electrolytic gap using Fluent software, revealing that an initial electrolyte velocity of 4 m/s and ultrasonic amplitude of 35 μm ensure optimal stability. High-speed photography is employed to capture bubble distribution and micro-pit formation dynamics, while SS304 surface experiments analyze the effects of machining parameters on micro-dimple localization and surface quality. The results show that optimized parameters significantly improve micro-texture quality, yielding micro-pits with a width of 223.4 μm, depth of 28.9 μm, aspect ratio of 0.129, and Ra of 0.205 μm, providing theoretical insights for high-precision metal micromachining. Full article

Background: Integrating artificial intelligence into clinical photography offers great potential for monitoring skin conditions such as actinic keratosis (AK) and skin field cancerization. Identifying the extent of AK lesions often requires more than analyzing lesion morphology—it also depends on contextual cues, such as surrounding photodamage. This highlights the need for models that can combine fine-grained local features with a comprehensive global view. Methods: To address this challenge, we propose AKTransU-net, a hybrid U-net-based architecture. The model incorporates Transformer blocks to enrich feature representations, which are passed through ConvLSTM modules within the skip connections. This configuration allows the network to maintain semantic coherence and spatial continuity in AK detection. This global awareness is critical when applying the model to whole-image detection via tile-based processing, where continuity across tile boundaries is essential for accurate and reliable lesion segmentation. Results: The effectiveness of AKTransU-net was demonstrated through comparative evaluations with state-of-the-art segmentation models. A proprietary annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis was used to train and evaluate the models. From each photograph, crops of 512 × 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts. AKtransU-net exhibited a more robust context awareness and achieved a median Dice score of 65.13%, demonstrating significant progress in whole-image assessments. Conclusions: Transformer-driven context modeling offers a promising approach for robust AK lesion monitoring, supporting its application in real-world clinical settings where accurate, context-aware analysis is crucial for managing skin field cancerization. Full article

Bagheria, located on the northern coast of Sicily, is home to one of the Mediterranean’s most remarkable ensembles of Baroque villas, constructed between the 17th and 18th centuries by the aristocracy of Palermo. Originally situated within a highly structured rural landscape of citrus groves, gardens, and visual axes, these monumental residences have undergone substantial degradation due to uncontrolled urban expansion throughout the 20th century. This study presents a diachronic spatial analysis of Bagheria’s territorial transformation from 1850 to 2018, integrating historical cartography, aerial photography, satellite imagery, and Geographic Information System (GIS) tools. A total of 33 villas were identified, georeferenced, and assessed based on their spatial integrity, architectural condition, and relationship with the evolving urban fabric. The results reveal a progressive marginalization of the villa system, with many heritage assets now embedded within dense residential development, severed from their original landscape context and deprived of their formal gardens and visual prominence. Comparative insights drawn from analogous Mediterranean heritage landscapes, such as Ortigia (Siracusa), the Appian Way (Rome), and Athens, highlight the urgency of adopting integrated conservation frameworks that reconcile urban development with cultural and ecological continuity. As a strategic response, the study proposes the creation of a thematic cultural route, La città delle ville, to enhance the visibility, accessibility, and socio-economic relevance of Bagheria’s heritage system. This initiative, supported by adaptive reuse policies, smart heritage technologies, and participatory planning, offers a replicable model for sustainable territorial regeneration and heritage-led urban resilience. Full article

Glaucoma is a leading cause of irreversible blindness globally, with early diagnosis being crucial to preventing vision loss. Traditional diagnostic methods, including fundus photography, OCT imaging, and perimetry, often fall short in sensitivity and fail to integrate structural and functional data. This study proposes a novel multi-modal diagnostic framework that combines convolutional neural networks (CNNs), vision transformers (ViTs), and quantum-enhanced layers to improve glaucoma detection accuracy and efficiency. The framework integrates fundus images, OCT scans, and clinical biomarkers, leveraging their complementary strengths through a weighted fusion mechanism. Datasets, including the GRAPE and other public and clinical sources, were used, ensuring diverse demographic representation and supporting generalizability. The model was trained and validated using cross-entropy loss, L2 regularization, and adaptive learning strategies, achieving an accuracy of 96%, sensitivity of 94%, and an AUC of 0.97—outperforming CNN-only and ViT-only approaches. Additionally, the quantum-enhanced architecture reduced computational complexity from O(n²) to O (log n), enabling real-time deployment with a 40% reduction in FLOPs. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. This framework offers a scalable and clinically viable tool for early glaucoma detection, supporting personalized care and improving diagnostic workflows in ophthalmology. Full article

This study focuses on the ancient buildings in Cicheng Old Town, a typical architectural heritage area in the Jiangnan region of China. These buildings are famous for their well-preserved Tang Dynasty urban layout and Ming and Qing Dynasty roof tiles. However, the natural aging, weathering, and biological erosion of the roof tiles seriously threaten the integrity of heritage protection. Given that current detection methods mostly depend on manual checks, which are slow and cover only a small area, this study suggests using deep learning technology for heritage protection and creating a smart model to identify damage in flat tiles using the YOLOv8 architecture. During this research, the team used drone aerial photography to collect images of typical building roofs in Cicheng Old Town. Through preprocessing, unified annotation, and system training, a damage dataset containing 351 high-quality images was established, covering five types of damage: breakage, cracks, the accumulation of fallen leaves, lichen growth, and vegetation growth. The results show that (1) the model has an overall mAP of 73.44%, an F1 value of 0.75 in the vegetation growth category, and a recall rate of 0.70, showing stable and balanced detection performance for various damage types; (2) the model performs well in comparisons using confusion matrices and multidimensional indicators (including precision, recall, and log-average miss rate) and can effectively reduce the false detection and missed detection rates; and (3) the research team applied the model to drone images of the roof of Fengyue Painted Terrace Gate in Cicheng Old Town, Jiangbei District, Ningbo City, Zhejiang Province, and automatically detected and located multiple tile damage areas. The prediction results are highly consistent with field observations, verifying the feasibility and application potential of the model in actual heritage protection scenarios. Full article

The world is characterised by the growing volumes and flow of goods, which, amid benefits to economic development, result in negative externalities affecting the sustainability of cities. Although numerous studies have analysed the locational patterns of logistics facilities in cities, further research is required to examine their real estate patterns and trends. The aim of the paper is, therefore, to analyse the value of land accommodating logistics facilities in the City of Cape Town municipality, South Africa. Given the lack of dedicated geo-spatial data, logistics firms were searched on Google Maps, utilising a combination of aerial photography and street view imagery. Three main attributes of land parcels hosting logistics facilities were thereafter captured from the municipal cadastral information: property extent, street address, and property number. The latter two were used to extract the 2018 and 2022 property market values from the valuation rolls on the municipal website, followed by statistical, spatial, and geographically weighted regression (GWR) analyses. Zones near the central business district and seaport, as well as areas with prime road-based accessibility, had high market values, while those near the railway stations did not stand out. However, GWR yielded weak relationships between market values and the locational variables analysed, arguably showing a disconnect between spatial planning and logistics planning. Towards augmenting sustainable logistics, it is recommended that relevant stakeholders strategically integrate logistics into spatial planning, and particularly revitalise freight rail to attract investment to logistics hubs with direct railway access. Full article

High Dynamic Range (HDR) imaging is capable of capturing vivid and lifelike visual effects, which are crucial for fields such as computer vision, photography, and medical imaging. However, real-time processing of HDR content remains challenging due to the computational complexity of tone mapping algorithms and the inherent limitations of Low Dynamic Range (LDR) capture systems. This paper presents an adaptive HDR tone mapping processor that achieves high computational efficiency and robust image quality under varying exposure conditions. By integrating an exposure-adaptive factor into a bilateral filtering framework, we dynamically optimize parameters to achieve consistent performance across fluctuating illumination conditions. Further, we introduce a high-radix Taylor expansion technique to accelerate floating-point logarithmic and exponential operations, significantly reducing resource overhead while maintaining precision. The proposed architecture, implemented on a Xilinx XCVU9P FPGA, operates at 250 MHz and processes 4K video at 30 frames per second (FPS), outperforming state-of-the-art designs in both throughput and hardware efficiency. Experimental results demonstrate superior image fidelity with an average Tone Mapping Quality Index (TMQI): 0.9314 and 43% fewer logic resources compared to existing solutions, enabling real-time HDR processing for high-resolution applications. Full article

Video stabilization is a critical technology for enhancing video quality by eliminating or reducing image instability caused by camera shake, thereby improving the visual viewing experience. It has deeply integrated into diverse applications—including handheld recording, UAV aerial photography, and vehicle-mounted surveillance. Propelled by advances in deep learning, data-driven stabilization methods have emerged as prominent solutions, demonstrating superior efficacy in handling jitter while achieving enhanced processing efficiency. This review systematically examines the field of video stabilization. First, this paper delineates the paradigm shift from classical to deep learning-based approaches. Subsequently, it elucidates conventional digital stabilization frameworks and their deep learning counterparts along with establishing standardized assessment metrics and benchmark datasets for comparative analysis. Finally, this review addresses critical challenges such as robustness limitations in complex motion scenarios and latency constraints in real-time processing. By integrating interdisciplinary perspectives, this work provides scholars with academically rigorous and practically relevant insights to advance video stabilization research. Full article

This study investigates the spatter suppression mechanism in aluminum alloy welding using a hybrid fiber–semiconductor laser system. By integrating high-speed photography and three-dimensional thermal-fluid coupling numerical simulations, the spatter formation process and its suppression mechanisms were systematically analyzed. The results indicate that spatter formation is primarily governed by surface tension and recoil pressure. In single fiber laser welding, concentrated laser energy induces a steep temperature gradient on the molten pool surface, triggering a strong Marangoni effect and subsequent spatter generation. In contrast, the hybrid laser system optimizes energy distribution, reducing the temperature gradient and weakening the Marangoni effect, thereby suppressing spatter. Additionally, the hybrid laser stabilizes molten pool flow through uniform recoil pressure distribution, further inhibiting spatter formation. Experimental results demonstrate that the hybrid fiber–semiconductor laser system significantly reduces spatter, improving welding quality and stability. This study provides theoretical and technical support for optimizing aluminum alloy laser welding. Full article

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article

Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under complex or extreme lighting. We propose SCR-AWB, a novel algorithm that leverages real skin reflectance data to estimate the scene illuminant’s SPD and CCT, enabling accurate skin tone reproduction. The method integrates prior knowledge of human skin reflectance, basis vectors, and camera sensitivity to perform pixel-wise spectral estimation. Experimental results on difficult skin color reproduction task demonstrate that SCR-AWB significantly outperforms traditional AWB algorithms. It achieves lower reproduction angle errors and more accurate CCT predictions, with deviations below 300 K in most cases. These findings validate SCR-AWB as an effective and computationally efficient solution for robust skin color correction. Full article