Search Results (562)

The conservation of architectural heritage (AH) in regions threatened by natural and human-induced factors requires interdisciplinary approaches that integrate physical documentation with semantic modeling. This study introduces a comprehensive framework combining Historic Building Information Modeling (HBIM) with ontology-based modeling aligned with the CIDOC Conceptual Reference Model (CIDOC CRM). Focusing on the Big Gate and adjacent curtain walls in Ibb, Yemen, where the gate is entirely lost, the study reconstructs the structure using historical photographs, eyewitness accounts, and analogical references. The methodology incorporates UAV and terrestrial photogrammetry surveys, point cloud generation, and semantic enrichment using Autodesk Revit V. 2024 and Protégé V. 5.5. Decay phenomena such as cracks, efflorescence, and disintegration were ontologically classified and spatially linked to the HBIM model, revealing deterioration patterns concerning historical phases and environmental exposure. The resulting system enables dynamic documentation, facilitates strategic conservation planning, and enhances data interoperability across heritage platforms. The proposed framework is transferable to other heritage sites, supporting both the conservation of extant structures and the reconstruction of lost ones. Full article

As part of contemporary urban culture, murals support place making and city identity. While much attention has been paid to their role in activating public space during daylight hours, their presence after dark remains largely unexamined. This paper analyzes how mural content interacts with night-time illumination. The research draws on case studies, photographs, luminance measurements, and lighting simulations. It evaluates how existing lighting systems support or undermine the legibility and impact of commercial murals in urban environments. It explores whether standardized architectural lighting guidelines suit murals, how color and surface affect visibility, and which practices improve night-time legibility. The study identifies a gap in existing lighting strategies, noting that uneven lighting distorts intent and reduces public engagement. In response, a new design tool—the Floodlighting Content Readability Map—is proposed to support artists and planners in creating night-visible murals. This paper situates mural illumination within broader debates on creative urbanism and argues that lighting is not just infrastructure, but a cultural and aesthetic tool that extends the reach and resonance of public art in the 24 h city. It further emphasizes the need for interdisciplinary collaboration and a multi-contextual perspective—encompassing visual, social, environmental, and regulatory dimensions—when designing murals in cities. Full article

This research introduces an automated 3D virtual reconstruction system tailored for architectural heritage (AH) applications, contributing to the ongoing paradigm shift from traditional CAD-based workflows to artificial intelligence-driven methodologies. It reviews recent advancements in machine learning and deep learning—particularly neural radiance fields (NeRFs) and its successor, Gaussian splatting (GS)—as state-of-the-art techniques in the domain. The study advocates for replacing point cloud data in heritage building information modeling workflows with image-based inputs, proposing a novel “photo-to-BIM” pipeline. A proof-of-concept system is presented, capable of processing photographs or video footage of ancient ruins—specifically, Romanesque–Mudéjar churches—to automatically generate 3D mesh reconstructions. The system’s performance is assessed using both objective metrics and subjective evaluations of mesh quality. The results confirm the feasibility and promise of image-based reconstruction as a viable alternative to conventional methods. The study successfully developed a system for automated 3D mesh reconstruction of AH from images. It applied GS and Mip-splatting for NeRFs, proving superior in noise reduction for subsequent mesh extraction via surface-aligned Gaussian splatting for efficient 3D mesh reconstruction. This photo-to-mesh pipeline signifies a viable step towards HBIM. Full article

Background: Chronic wounds represent a persistent clinical challenge and impose a considerable burden on healthcare systems. These lesions often require multidisciplinary management due to underlying factors such as microbial colonization, impaired immunity, and vascular insufficiencies. Regenerative therapies, particularly autologous approaches, have emerged as promising strategies to enhance wound healing. Adipose tissue-derived stem cells (ADSCs) and platelet-rich plasma (PRP) may improve outcomes through paracrine effects and growth factor release. Methods: A prospective observational study was conducted on 31 patients with chronic wounds that were unresponsive to conservative treatment for over six weeks. Clinical and photographic evaluations were employed to monitor healing. All patients underwent surgical debridement, with adjunctive interventions—negative pressure wound therapy, grafting, or flaps—applied as needed. PRP infiltration and/or autologous adipose tissue transfer were administered based on wound characteristics. Wound area reduction was the primary outcome measure. Results: The cohort included 17 males and 14 females (mean age: 59 years). Etiologies included venous insufficiency (39%), diabetes mellitus (25%), arterial insufficiency (16%), and trauma (16%). Most lesions (84%) were located on the lower limbs. All patients received PRP therapy; five underwent combined PRP and fat grafting. Over the study period, 64% of the patients exhibited >80% wound area reduction, with complete healing in 48.3% and a mean healing time of 49 days. Conclusions: PRP therapy proved to be a safe, effective, and adaptable treatment, promoting substantial healing in chronic wounds. Autologous adipose tissue transfer did not confer additional benefit. PRP may warrant inclusion in national treatment protocols. Full article

To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital images of single yarns. The yarn and background are segmented using the K-means clustering algorithm, and the centerline of the yarn is extracted using a skeletonization algorithm. Spectral reconstruction and colorimetric principles are then applied to calculate the color values of pixels along the centerline. Considering the nonlinear characteristics of human brightness perception, the final yarn color is obtained through a nonlinear texture-adaptive weighted computation. The method is validated through psychophysical experiments using six yarns of different colors and compared with spectrophotometry and five other photographic measurement methods. Results indicate that among the seven yarn color measurement methods, including spectrophotometry, the proposed method—based on centerline extraction and nonlinear texture-adaptive weighting—yields results that more closely align with actual visual perception. Furthermore, among the six photographic measurement methods, the proposed method produces most similar to those obtained using spectrophotometry. This study demonstrates the inconsistency between spectrophotometric measurements and human visual perception of yarn color and provides methodological support for developing visually consistent color measurement methods for textured textiles. Full article

Over the past 50 years, coastal erosion has become an increasingly critical issue worldwide, and Colombia’s Caribbean coast is no exception. In urban areas, this challenge is further complicated by hard protection structures, which, while often implemented as immediate solutions, can disrupt sediment transport and trigger unintended long-term consequences. This study examines shoreline changes in Riohacha, the capital of La Guajira Department, over a 35-year period (1987–2022), focusing on the impacts of coastal protection structures—specifically, the construction of seven groins and a seawall between 2006 and 2009—on coastal dynamics. Using twelve images (photographs and satellite) and the Digital Shoreline Analysis System (DSAS), the evolution of both beaches and cliffs has been analyzed. The results reveal a dramatic shift in shoreline behavior: erosion rates of approximately 0.5 m/year prior to the interventions transitioned to accretion rates of up to 11 m/year within the groin field, where rapid infill occurred. However, this sediment retention has exacerbated erosion in downstream cliff areas, with retreat rates reaching 1.8 ± 0.2 m/year. To anticipate future coastal evolution, predictive models were applied through 2045, providing insights into potential risks for infrastructure and urban development. These findings highlight the need for a strategic, long-term approach to coastal management that considers both the benefits and unintended consequences of engineering interventions. Full article

Background/Objectives: Discrepancies in diabetic retinopathy (DR) grading are well-documented, with retinal non-perfusion (RNP) quantification posing greater challenges. This study assessed intergrader agreement in DR evaluation, focusing on qualitative severity grading and quantitative RNP measurement. We aimed to improve agreement through structured consensus meetings. Methods: A retrospective analysis of 100 comparisons from 50 eyes (36 patients) was conducted. Two paired medical retina fellows graded ultra-widefield color fundus photographs (CFP) and fundus fluorescein angiography (FFA) images. CFP assessments included DR severity using the International Clinical Diabetic Retinopathy (ICDR) grading system, DR Severity Scale (DRSS), and predominantly peripheral lesions (PPL). FFA-based RNP was defined as capillary loss with grayscale matching the foveal avascular zone. Weekly adjudication by a senior specialist resolved discrepancies. Intergrader agreement was evaluated using Cohen’s kappa (qualitative DRSS) and intraclass correlation coefficients (ICC) (quantitative RNP). Bland–Altman analysis assessed bias and variability. Results: After eight consensus meetings, CFP grading agreement improved to excellent: kappa = 91% (ICDR DR severity), 89% (DRSS), and 89% (PPL). FFA-based PPL agreement reached 100%. For RNP, the non-perfusion index (NPI) showed moderate overall ICC (0.49), with regional ICCs ranging from 0.40 to 0.57 (highest in the nasal region, ICC = 0.57). Bland–Altman analysis revealed a mean NPI difference of 0.12 (limits: −0.11 to 0.35), indicating acceptable variability despite outliers. Conclusions: Structured consensus training achieved excellent intergrader agreement for DR severity and PPL grading, supporting the clinical reliability of ultra-widefield imaging. However, RNP measurement variability underscores the need for standardized protocols and automated tools to enhance reproducibility. This process is critical for developing robust AI-based screening systems. Full article

Biosensors play a central role in the early detection of abnormal glucose levels in individuals with diabetes; therefore, the development of less invasive systems is essential. Herein, a 3D-printed colorimetric biosensor combining microneedles and chitosan nanoparticles was developed for glucose detection in sweat using machine learning. Briefly, hollow 3D-printed polylactic acid microneedles were constructed and loaded with chitosan nanoparticles encapsulating glucose oxidase, horseradish peroxidase, and the chromogenic substrate 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), resulting in the formation of the chitosan nanoparticle−microneedle patches. Glucose detection was performed colorimetrically by first incubating the chitosan nanoparticle−microneedle patches with glucose samples of varying concentrations and then by using photographs of the top side of each microneedle and a color recognition application on a smartphone. The Random Sample Consensus algorithm was used to train a simple linear regression model to predict glucose concentrations in unknown samples. The developed biosensor system exhibited a good linear response range toward glucose (0.025−0.375 mM), a low limit of detection (0.023 mM), a limit of quantification (0.078 mM), high specificity, and recovery rates ranging between 86–112%. Lastly, the biosensor was applied to glucose detection in spiked artificial sweat samples, confirming the potential of the proposed methodology for glucose detection in real samples. Full article

Background: Reliable and non-invasive methods for assessing welfare in poultry are essential for improving evidence-based welfare monitoring and advancing management practices in commercial production systems. The iris-to-pupil (IP) ratio, previously validated by our group in primates and cattle, reflects autonomic nervous system balance and may serve as a physiological indicator of stress in laying hens. This study evaluated the utility of the IP ratio under field conditions across diverse commercial layer housing systems. Materials and Methods: In total, 296 laying hens (Lohmann Brown, n = 269; White Leghorn, n = 27) were studied across four locations in Canada housed under different systems: Guelph (indoor; pen), Spring Island (outdoor and scratch; organic), Ottawa (outdoor, indoor and scratch; free-range), and Toronto (outdoor and hobby; free-range). High-resolution photographs of the eye were taken under ambient lighting. Light intensity was measured using the light meter app. The IP ratio was calculated using NIH ImageJ software (Version 1.54p). Statistical analysis included one-way ANOVA and linear regression using GraphPad Prism (Version 5). Results: Birds housed outdoors had the highest IP ratios, followed by those in scratch systems, while indoor and pen-housed birds had the lowest IP ratios (p < 0.001). Subgroup analyses of birds in Ottawa and Spring Island farms confirmed significantly higher IP ratios in outdoor environments compared to indoor and scratch systems (p < 0.001). The IP ratio correlated weakly with ambient light intensity (r² = 0.25) and age (r² = 0.05), indicating minimal influence of these variables. Although White Leghorn hens showed lower IP ratios than Lohmann Browns, this difference was confounded by housing type; all White Leghorns were housed in pens. Thus, housing system but not breed was the primary driver of IP variation. Conclusions: The IP ratio is a robust, non-invasive physiological marker of welfare assessment in laying hens, sensitive to housing environment but minimally influenced by light or age. Its potential for integration with digital imaging technologies supports its use in scalable welfare assessment protocols. Full article

In order to solve the zoom delay issue for high-magnification zoom optical systems, a voice coil motor (VCM) is used to achieve rapid zooming. In this paper, the structural design of VCMs is systematically analyzed through magnetic field numerical computations. Firstly, finite element method (FEM) is used to analyze magnetic field of single magnets, and simulations correspond to experimental results. Both FEM and equivalent magnetic charge (EMC) results confirm that increasing magnet thickness while reducing its lateral dimensions will contribute to magnetic enhancement. Furthermore, the influence of structural parameters VCM is analyzed, validating the yoke’s critical role in suppressing edge effects and optimizing magnetic circuit efficiency, and optimal yoke thickness and magnet width range are determined. Moreover, a simple EMC calculation method is proposed for rapid and accurate determination of the magnetic field distribution in the VCM air gap. Optimal structural parameters of VCM are determined for a 40× rapid zoom lens with cost and space limitations. Driving force F_drive = 5.58 N is about 5 times the demand force F_d = 1.06 N, and the prototype fabrication of the rapid zoom lens is successfully accomplished. Moving group reaches 35.4 mm destination within 0.18 s, and photographs confirm that the rapid zoom system achieves 100-ms-level short/long-focus transition. Rapid zoom lens shows great potential in applications including security surveillance, industrial visual inspection, and intelligent logistics management. Full article

This study presents the first comprehensive nationwide assessment of mangrove ecosystems in the Maldives. Surveys were conducted across 162 islands in 20 administrative atolls, integrating field data, the literature, and secondary sources to map mangrove distribution, confirm species presence, and classify habitat types. Twelve true mangrove species were identified, with Bruguiera cylindrica, Rhizophora mucronata, and Lumnitzera racemosa emerging as dominant. Species diversity was evaluated using Shannon (H′), Margalef (d′), Pielou’s evenness (J′), and Simpson’s dominance (λ′) indices. Atolls within the northern and southern regions, particularly Laamu, Noonu, and Shaviyani, exhibited the highest diversity and evenness, while central atolls such as Ari and Faafu supported mono-specific or degraded stands. Mangrove habitats were classified into four geomorphological types: marsh based, pond based, embayment, and fringing systems. Field sampling was conducted using standardized belt transects and quadrats, with species verified using photographic documentation and expert validation. Species distributions showed strong habitat associations, with B. cylindrica dominant in marshes, R. mucronata and B. gymnorrhiza in ponds, and Ceriops tagal and L. racemosa in embayments. Rare species like Bruguiera hainesii and Heritiera littoralis were confined to stable hydrological niches. This study establishes a critical, island-level baseline for mangrove conservation and ecosystem-based planning in the Maldives, providing a reference point for tracking future responses to climate change, sea-level rise, and hydrological disturbances, emphasizing the need for habitat-specific strategies to protect biodiversity. Full article

Lethocerus patruelis (Stål, 1854) is a large aquatic hemipteran and the only European representative of the family Belostomatidae. Commonly known as the giant water bug, this species was historically restricted to the Balkans, Anatolia, and parts of the Middle East, but has exhibited a marked westward and northward range expansion in recent decades. In this study, we report the first confirmed occurrence of L. patruelis on Lesvos Island, in the northeastern Aegean Sea, based on a direct observation made within a wastewater treatment facility. The individual was identified in situ using diagnostic morphological traits and photographed without disturbance. This finding extends the known insular distribution of the species and underscores its capacity to exploit anthropogenically modified aquatic systems. Given the island’s rich mosaic of natural and artificial wetland habitats—including over 200 mapped sites—Lesvos may offer suitable conditions for the establishment of local populations. This record highlights the need for targeted surveys and long-term monitoring across under-sampled insular landscapes. Full article

The growing relevance of sustainable practices has driven organizations from various sectors to adapt their activities to current socio-environmental demands. In the construction sector, this demand is even more pronounced due to the high consumption of natural resources and the significant generation of solid waste. However, questions remain about the extent to which companies in this sector understand and incorporate sustainable practices into their routines. This study investigates the level of knowledge and the adoption of sustainable practices by residential building construction companies registered with the Civil Construction Industry Union of Mossoró/RN. A qualitative-quantitative approach was adopted, using questionnaires and photographic records collected during on-site visits. The data reveal an incipient adoption of Environmental Management Systems (EMSs) and limited knowledge about ESG principles, highlighting structural and cultural barriers to sustainability in the sector. Nevertheless, isolated initiatives related to waste reduction and the adoption of more efficient practices were observed. The study concludes that strengthening technical training, promoting management systems, and aligning with contemporary demands are relevant strategies to foster sustainability and competitiveness in the construction sector. Full article

Clinicopathological studies and the effectiveness of dopaminergic replacement therapy establish that dopamine loss is the key pathology causing motor symptoms in Parkinson’s disease. The dopamine neurons that are impaired in Parkinson’s disease reside in the substantia nigra and ventral tegmental area in the midbrain. These neurons project into the striatum, where dopamine axons bifurcate repeatedly and form dense axon networks (the striatum is separated into the caudate nucleus and putamen by the internal capsule). Midbrain dopamine neurons also innervate many other areas of the brain, including the cerebral cortex. Therefore, there are preclinical and clinical studies investigating extrastriatal dopamine mechanisms in motor control and Parkinson’s disease pathophysiology and treatment. While extrastriatal dopamine can contribute, this contribution needs to be compared with the contribution of the striatal dopamine system. An isolated view of the extrastriatal dopamine system is like examining only the ear of an elephant and may lead to distorted assessments for preclinical and clinical research and diagnostic work. Thus, photographs of the whole brain dopamine system are important. For these reasons, we photographed the dopamine systems in whole mouse brain sagittal sections, showing clearly that, under identical imaging conditions, dopamine innervation is highly concentrated and intense in the striatum but sparse and weak in the cerebral cortex. Full article

The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making. Full article