Search Results (287)

Unmanned Aerial Vehicles (UAVs) equipped with onboard cameras and deep-learning-based object detection algorithms are increasingly used in search operations. This study investigates the optimal flight parameters, specifically flight speed and ground sampling distance (GSD), to maximize a search efficiency metric called effective coverage. A custom dataset of 4468 aerial images with 35,410 annotated cardboard targets was collected and used to evaluate the influence of flight conditions on detection accuracy. The effects of flight speed and GSD were analyzed using regression modeling, revealing a trade-off between the area coverage and detection confidence of trained YOLOv8 and YOLOv11 models. Area coverage was modeled based on flight speed and camera specifications, enabling an estimation of the effective coverage. The results provide insights into how the detection performance varies across different operating conditions and demonstrate that a balance point exists where the combination of the detection reliability and coverage efficiency is optimized. Our table of the optimal flight regimes and metrics for the most commonly used cameras in UAV operations offers practical guidelines for efficient and reliable mission planning. Full article

Achieving energy-efficient aerial coverage remains a significant challenge for UAV-based missions, especially over hilly terrain where consistent ground resolution is needed. Traditional solutions use changes in altitude to compensate for elevation changes, which requires a significant amount of energy. This paper presents a new way to plan coverage paths (CPP) that uses real-time zoom control of a pan–tilt–zoom (PTZ) camera to keep the ground sampling distance (GSD)—the distance between two consecutive pixel centers projected onto the ground—constant without changing the UAV’s altitude. The proposed algorithm changes the camera’s focal length based on the height of the terrain. It only changes the altitude when the zoom limits are reached. Simulation results on a variety of terrain profiles show that the zoom-based CPP substantially reduces flight duration and path length compared to traditional altitude-based strategies. The framework can also be used with low-cost camera systems with limited zoom capability, thereby improving operational feasibility. These findings establish a basis for further development and field validation in upcoming research phases. Full article

Background: Off-label nebulization of tranexamic acid (TXA) solution is common practice for the treatment of hemoptysis. However, data regarding nebulization protocols, resulting aerodynamic parameters of the generated aerosol, and corresponding biopharmaceutical parameters are missing. The aim of this in vitro study was to investigate the aerosol characteristics of nebulized sterile, aqueous TXA solution. Methods: TXA solution 100 mg/mL was nebulized for 2 min by a multi-dose vibrating mesh nebulizer using 15 L/min and 30 L/min air flow rates. The generated aerosol was analyzed by a Next Generation Cascade Impactor. For each air flow rate, the mean Fine Particle Dose (FPD), Fine Particle Fraction (FPF), the Mass Median Aerodynamic Diameter (MMAD), and Geometric Standard Deviation (GSD) were quantified. Results: Nebulization at 15 L/min air flow rate resulted in a MMAD of 6.68 ± 0.23 µm and GSD of 2.02 ± 0.16. The FPD < 5 µm was 16.56 ± 0.45 mg, the FPF < 5 µm 28.91 ± 3.40%. Nebulization at 30 L/min air flow rate revealed a MMAD of 5.18 ± 0.12 µm and GSD of 2.14 ± 0.10. The FPD < 5 µm was 16.30 ± 1.38 mg, the FPF < 5 µm 35.43 ± 0.59%. Conclusions: Nebulization of TXA 100 mg/mL solution by a specified vibrating mesh nebulizer generated an aerosol particle distribution and deposition pattern suitable for the treatment of hemoptysis with bronchial origin. Full article

The accurate monitoring of waterbird abundance and their habitat preferences is essential for effective ecological management and conservation planning in aquatic ecosystems. This study explores the efficacy of unmanned aerial vehicle (UAV)-based high-resolution orthomosaics for waterbird monitoring and mapping along the Lieve Canal, Belgium. We systematically classified habitats into residential, industrial, riparian tree, and herbaceous vegetation zones, examining their influence on the spatial distribution of three focal waterbird species: Eurasian coot (Fulica atra), common moorhen (Gallinula chloropus), and wild duck (Anas platyrhynchos). Herbaceous vegetation zones consistently supported the highest waterbird densities, attributed to abundant nesting substrates and minimal human disturbance. UAV-based waterbird counts correlated strongly with ground-based surveys (R² = 0.668), though species-specific detectability varied significantly due to morphological visibility and ecological behaviors. Detection accuracy was highest for coots, intermediate for ducks, and lowest for moorhens, highlighting the crucial role of image resolution ground sampling distance (GSD) in aerial monitoring. Operational challenges, including image occlusion and habitat complexity, underline the need for tailored survey protocols and advanced sensing techniques. Our findings demonstrate that UAV imagery provides a reliable and scalable method for monitoring waterbird habitats, offering critical insights for biodiversity conservation and sustainable management practices in aquatic landscapes. Full article

With coastal tourism depending on clean beaches and litter surveys remaining manual, sparse, and costly, this study coupled centimeter-resolution UAV imagery with a Grid R-CNN detector to automate debris mapping on five beaches of Wonsan Island, Korea. Thirty-one Phantom 4 flights (0.83 cm GSD) produced 31,841 orthoimages, while 11 debris classes from the AI Hub dataset trained the model. The network reached 74.9% mAP and 78%/84.7% precision–recall while processing 2.87 images s⁻¹ on a single RTX 3060 Ti, enabling a 6 km shoreline to be surveyed in under one hour. Georeferenced detections aggregated to 25 m grids showed that 57% of high-density cells lay within 100 m of the beach entrances or landward edges, and 86% within 200 m. These micro-patterns, which are difficult to detect in meter-scale imagery, suggest that entrance-focused cleanup strategies could reduce annual maintenance costs by approximately one-fifth. This highlights the potential of centimeter-scale GeoAI in supporting sustainable beach management. Full article

Grape (Vitis vinifera L.) is one of the most extensively cultivated crops in temperate climates, with its primary fate being wine production, which is paired with a great generation of grape pomace (GP). GP contains a plethora of antioxidant phenolic compounds, being well-known for its high content of various tannins, liable for the astringency of this fruit. Winemaking produces a great mass of by-products that are rich in tannins. Grape seed (GSd) and pulp waste, as well as leaves and stems (GSt), are rich in condensed tannins (CTs), while its skin (GSk) contains more flavonols and phenolic acids. CTs are polymers of flavan-3-ols, and their antioxidant and anti-inflammatory properties are well-accounted for, being the subject of extensive research for various applications. CTs from the diverse fractions of grapefruit and grapevine share similar structures given their composition but diverge in their degree of polymerization, which can modulate their chemical interactions and may be present at around 30 to 80 mg/g, depending on the grape fraction. Thus, this prominent agroindustrial by-product, which is usually managed as raw animal feed or further fermented for liquor production, can be valorized as a source of tannins with high added value. The present review addresses current knowledge on tannin diversity in grapefruit and grapevine by-products, assessing the differences in composition, quantity, and degree of polymerization. Current knowledge of their reported bioactivities will be discussed, linking them to their current and potential applications in food and feed. Full article

Structural cracks can critically undermine infrastructure integrity, driving the need for precise, scalable inspection methods beyond conventional visual or 2D image-based approaches. This study presents an automated system integrating photogrammetric 3D reconstruction with deep learning to quantify crack dimensions in a spatial context. Multiple images are processed via Agisoft Metashape to generate high-fidelity 3D meshes. Then, a subset of images are automatically selected based on camera orientation and distance, and a deep learning algorithm is applied to detect cracks in 2D images. The detected crack edges are projected onto a 3D mesh, enabling width measurements grounded in the structure’s true geometry rather than perspective-distorted 2D approximations. This methodology addresses the key limitations of traditional methods (parallax, occlusion, and surface curvature errors) and shows how these limitations can be mitigated by spatially anchoring measurements to the 3D model. Laboratory validation confirms the system’s robustness, with controlled tests highlighting the importance of near-orthogonal camera angles and ground sample distance (GSD) thresholds to ensure crack detectability. By synthesizing photogrammetry and a convolutional neural network (CNN), the framework eliminates subjectivity in inspections, enhances safety by reducing manual intervention, and provides engineers with dimensionally accurate data for maintenance decisions. Full article

Background/Objectives: Hepatic glycogen storage diseases (GSDs) are inherited metabolic disorders that affect glycogen synthesis or breakdown, primarily involving the liver and muscles. Treatment typically consists of strict dietary management, including the consumption of uncooked cornstarch. However, there is limited research on feeding challenges and the associated stress experienced by parents of children with GSDs. This study aims to assess feeding difficulties in children with GSDs and the level of parental stress. Methods: A total of 29 caregivers of children aged 6 months to <7 years participated. Feeding difficulties were evaluated using the Brazilian Infant Feeding Scale (Escala Brasileira de Alimentação Infantil—EBAI), while parental stress was measured using the Parental Stress Scale (Escala de Estresse Parental—EEPa). Data were collected in 2020, and the study was approved by the ethics committee. Results: The majority of the children were male (19/10), with a mean age of 47.75 months and an average age of diagnosis of 8.39 months. GSD type Ia (n = 15) and type Ib (n = 5) were the most prevalent, followed by types III and IX (n = 2). Among the participants, 22 out of 29 (76%) reported feeding difficulties, categorized as mild (n = 7, 24%), moderate (n = 7, 24%), and severe (n = 8, 28%). EBAI scores were higher in female patients and in those who did not eat meals with their family. Only one caregiver exhibited high levels of parental stress, as measured by the EEPA scale. No significant correlation was found between feeding difficulties and parental stress. Conclusions: The findings confirm a high prevalence of feeding issues in children with GSDs, which significantly affects caregivers’ quality of life. Although no significant link between feeding difficulties and parental stress was identified, further research is needed to improve GSD management and provide better support for caregivers. Full article

Background/Objectives: Glycogen storage disease type IX (GSD IX) is a group of inherited metabolic disorders caused by phosphorylase kinase deficiency affecting the liver or muscle. Despite being relatively common among GSDs, GSD IX remains underexplored. Methods: A systematic review of GSD IX was conducted per PRISMA guidelines using SCOPUS and PubMed, registered with PROSPERO. Inclusion focused on human clinical studies published up to 31 December 2024. Results: A total of 400 patients with GSD IX were analyzed: 274 IXa (mean age at diagnosis 5.1 years), 72 IXc (mean age at diagnosis 4.9 years), 39 IXb (mean age at diagnosis 4.2 years), and 15 IXd (mean age at diagnosis 44.9 years). Hepatomegaly was commonly reported in types IXa, IXb, and especially IXc (91.7%), but was rare in IXd. Elevated transaminases were frequently observed in types IXa, IXb, and particularly IXc, while uncommon in IXd. Fasting hypoglycemia was occasionally observed in types IXa and IXb, more frequently in IXc (52.7%), and was not reported in IXd. Growth delay or short stature was observed in a substantial proportion of patients with types IXa (43.8%), IXb, and IXc, but was rare in IXd. Muscle involvement was prominent in IXd, with all patients showing elevated CPK (mean 1011 U/L). Neurological involvement was infrequently reported in types IXa and IXc. Conclusions: This systematic review includes the most extensive clinical case history of GSD IX described in the literature. The clinical spectrum of GSD IX varies widely among subtypes, with IXc being the most aggressive. While liver forms are generally present in early childhood, muscle-type IXd shows delayed onset and milder symptoms, often leading to diagnostic delays. For diagnosis, it is essential not to underestimate key clinical features such as hepatic involvement and hypoglycemia in a child under 5 years of age. Other manifestations, including the as-yet unexplored systemic involvement of bone and kidney, remain insufficiently understood and require further investigation. Next-generation sequencing has improved diagnostic precision over traditional biopsy. Dietary management, including uncooked cornstarch, Glycosade^®, and high-protein intake, remains the cornerstone of treatment. However, there is a paucity of well-designed, evidence-based studies to determine the most effective therapeutic approach. Despite its historically perceived benign course, the broad phenotypic variability of GSD IX, including progressive liver involvement and potential neurological complications, highlights its substantial clinical relevance and underscores the need for accurate diagnostic classification and long-term multidisciplinary follow-up. Full article

Graph neural networks (GNNs) have exhibited remarkable performance in various applications. Still, research has revealed their vulnerability to backdoor attacks, where Adversaries inject malicious patterns during the training phase to establish a relationship between backdoor patterns and a specific target label, thereby manipulating the behavior of poisoned GNNs. The inherent symmetry present in the behavior of GNNs can be leveraged to strengthen the robustness of GNNs. This paper presents a quantitative metric, termed Logit Margin Rate (LMR), for analyzing the symmetric properties of the output landscapes across GNN layers. Additionally, a learning paradigm of graph self-distillation is combined with LMR to distill the symmetry knowledge from shallow layers, which can serve as the defensive supervision signals to preserve the benign symmetric relationships in deep layers, thus improving both model stability and adversarial robustness. Experiments were conducted on four benchmark datasets to evaluate the robustness of the proposed Graph Self-Distillation-based Backdoor Defense (GSD-BD) method against three widely used backdoor attack algorithms, demonstrating the robustness of GSD-BD even under severe infection scenarios. Full article

The generation of digital aerial photogrammetry products using unmanned aerial vehicle-digital aerial photogrammetry (UAV-DAP) has become an essential task due to the increasing use of UAVs in the world of geomatics, thanks to their low cost and spatial resolution. Therefore, it is relevant to explore the performance of new digital cameras equipped in UAVs using electronic rolling shutters instead of ideal mechanical or global shutter cameras to achieve accurate and reliable photogrammetric products, if possible, while minimizing workload, especially for their application in projects that require a high level of detail. In this paper, we analyse performance using oblique images along the perimeter (3D perimeter) on a flat area, i.e., with slopes of less than 3%. The area was photogrammetrically surveyed with a DJI (Dà-Jiāng Innovations) Inspire 2 multirotor UAV equipped with a Zenmuse X5S rolling shutter camera. The photogrammetric survey was accompanied by a Global Navigation Satellite System (GNSS) survey, in which dual frequency receivers were used to determine the ground control points (GCPs) and checkpoints (CPs). The study analysed different scenarios, including the combination of forward and transversal strips and oblique images. After examining the ideal scenario with the least root mean square error (RMSE), six different combinations were analysed to find the best location for the GCPs. The most significant results indicate that the optimal calibration of the camera is obtained in scenarios including oblique images, which outperform the rest of the scenarios for achieving the lowest RMSE (2.5x the GSD in Z and 3.0x the GSD in XYZ) with optimum GCPs layout; with non-ideal GCPs layout, unacceptable errors can be achieved (11.4x the GSD in XYZ), even with ideal block geometry. The UAV-DAP rolling shutter effect can only be minimised in the scenario that uses oblique images and GCPs at the edges of the overlapping zones and the perimeter. Full article

Adaptive clinical trials offer a flexible approach for refining sample sizes during ongoing research to enhance their efficiency. This study delves into improving sample size recalculation through resampling techniques, employing measurement error and mixed distribution models. The research employs diverse sample size-recalculation strategies standard simulation, R1 and R2 approaches where R1 considers the mean and R2 employs both mean and standard deviation as summary locations. These strategies are tested against observed conditional power (OCP), restricted observed conditional power (ROCP), promising zone (PZ) and group sequential design (GSD). The key findings indicate that the R1 approach, capitalizing on mean as a summary location, outperforms standard recalculations without resampling as it mitigates variability in recalculated sample sizes across effect sizes. The OCP exhibits superior performance within the R1 approach compared to ROCP, PZ and GSD due to enhanced conditional power. However, a tendency to inflate the initial stage’s sample size is observed in the R1 approach, prompting the development of the R2 approach that considers mean and standard deviation. The ROCP in the R2 approach demonstrates robust performance across most effect sizes, although GSD retains superiority within the R2 approach due to its sample size boundary. Notably, sample size-recalculation designs perform worse than R1 for specific effect sizes, attributed to inefficiencies in approaching target sample sizes. The resampling-based approaches, particularly R1 and R2, offer improved sample size recalculation over conventional methods. The R1 approach excels in minimizing recalculated sample size variability, while the R2 approach presents a refined alternative. Full article

Deep gravitational slope deformations (DsGSDs) are a geological and engineering challenge with important implications for slope stability, the reliability of existing infrastructures, land use and, above all, the safety of settlements. This paper focuses on the DsGSD phenomenon that affects a large part of the Borrano hamlet, located in the municipality of Civitella del Tronto (Abruzzo Region, Central Italy). This instability is characterized by slow movements of large volumes of material. The main factors initiating deformations are a combination of geological and hydrogeological aspects. These factors include the complex local stratigraphy, composed of pelitic and arenaceous facies at high slope dip angles, and extreme natural events such as heavy rainfall and earthquakes. This study employs a multidisciplinary approach integrating in field activities such as remote-controlled surface monitoring (clinometers and strain gauges), in-depth monitoring (inclinometers and piezometers), aero-photogrammetric analysis and numerical modelling. These techniques permitted us to characterize the evolution of the slope and to identify both the critical sliding surfaces and the mechanisms governing the ground movements. Soil deformations were mainly observed in the central zone of the hamlet. Significant deformations were recorded along planes of weakness at depth between arenaceous and pelitic materials. These planes represent contact zones between the clayey–marly facies, characterized by low strength, and the arenaceous facies, characterized by higher stiffness, creating a mechanical contrast that favours the development of large deformations. The numerical analyses confirmed good correlation with the monitoring data, revealing in detail the instability of both local and territorial processes. The 3D numerical analysis showed how the movements are controlled by planes of weakness, highlighting the key rule of geological discontinuities. Full article

Multifractal analysis has been used in the exploration of soil grain size distributions (GSDs) in environmental and agricultural research. However, multifractal studies regarding the GSDs of sediments in braided delta-front are currently scarce. Open-source software designed for the realization of this technique has not yet been programmed. In this paper, the multifractal parameters of 61 GSDs from braided delta-front in the Paleogene Enping Formation in Huilu Low Uplift, Pearl River Mouth basin, are calculated and compared with traditional parameters. Multifractal generalized dimension spectrum curves are sigmoidal and decrease monotonically. Multifractal singularity spectrum curves are asymmetric, convex, and right-hook unimodal. The entropy dimension and singularity spectrum width ranges of silt-mudstones and gravelly sandstones are wider than those of fine and medium-coarse sandstones. The symmetry degree scopes from different lithologies are concentrated in distinguishing intervals. With the increase of grain sizes, the symmetry degree decreases overall. Both the symmetry degree and mean of GSDs are effective to distinguish the different lithologies from various depositional environments. A flexible and easy-to-use MATLAB (2021b)^® GUI (graphic user interface) package, MfGSD (Multifractal of GSD, V1.0), is provided to perform multifractal analysis on sediment GSDs. After raw GSDs imported into MfGSD, multifractal parameters are batch calculated and graphed in the interface. Then, all multifractal parameters can be exported to an Excel file, including entropy dimension, singularity spectrum, correlation dimension, symmetry degree of multifractal spectrum, etc. MfGSD is effective, and the multifractal parameters outputted from MfGSD are helpful to distinguish depositional environments of GSDs. MfGSD is open-source software that can be used to explore GSDs from various kinds of depositional environments, including water or wind deposits. Full article

The use of UAVs equipped with sensors has gained prominence due to their efficiency, safety, and ability to provide detailed data for hard-to-reach inspections, allowing for more precise and integrated analyses. Despite their great potential, these technologies still face a significant gap in understanding the actual capabilities of each piece of equipment to identify different pathological manifestations on building façades, as well as their advantages and limitations. In this context, this study aimed to analyze the capabilities and limitations of RGB, thermal, and multispectral sensors embedded in UAVs for detecting pathologies on building façades, addressing gaps in understanding their advantages, limitations, and effectiveness in identifying different anomalies. To this end, four façades of a building were mapped using these sensors, focusing on capturing pathologies such as cracks, moisture stains, coating detachment, and the presence of moss. The methodology involved using a UAV equipped with high-resolution RGB sensors (with GSD ranging from 0.867 mm to 0.985 mm per pixel), thermal sensors (with GSD ranging from 6.13 mm to 8.72 mm per pixel), and multispectral sensors (with GSD ranging from 6.49 mm to 9.80 mm per pixel), followed by the processing and creation of façade orthophotos and the visual identification of each pathology found on each face. Multiple image overlaps were performed to ensure adequate coverage of the analyzed surfaces. As a quantitative result, it was observed that the RGB sensor was the most effective in identifying surface pathologies, while thermal and multispectral sensors, although placing a smaller quantity of pathologies, stood out in detecting subsurface pathologies, such as infiltration and internal cracks. Complementarily, it was observed that the integration and complementarity of these devices could be of the utmost importance for a more comprehensive and precise analysis of pathologies, showing that combining different types of sensors significantly contributes to a more complete and detailed mapping of façade conditions. Full article