Search Results (1,524)

Drainage pipelines face significant threats to underground safety due to external defects. Ground Penetrating Radar (GPR) is a primary tool for detecting such defects from within the pipeline. However, existing methods are limited to single or multiple axial scan lines, which cannot provide the precise spatial coordinates of the defects. To address this limitation, this study introduces a novel GPR-based drainage pipeline inspection robot system integrated with multiple sensors. The system incorporates MEMS-IMU, encoder modules, and ultrasonic ranging modules to control the GPR antenna for axial and circumferential scanning. A novel Cross-Line Fusion of GPR (CLF-GPR) method is introduced to integrate axial and circumferential scan data for the precise localization of external pipeline defects. Laboratory simulations were performed to assess the effectiveness of the proposed technology and method, while its practical applicability was further validated through real-world drainage pipeline inspections. The results demonstrate that the proposed approach achieves axial positioning errors of less than 2.0 cm, spatial angular positioning errors below 2°, and depth coordinate errors within 2.3 cm. These findings indicate that the proposed approach is reliable and has the potential to support the transparency and digitalization of urban underground drainage networks. Full article

Realistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter. However, there has been limited effort to identify the sources of errors and contributions quantitatively using process-based backscatter simulation in comparison with extensive ground observations. This study examined the influence of input uncertainties on simulated backscatter from a first-order radiative transfer model, named the Wheat Canopy Scattering Model (WCSM), using ground-based and airborne data collected during the SMAPVEX12 campaign. Input uncertainties to WCSM were simulated using error statistics for two crop growth stages. The Sobol’ method was adopted to analyze the uncertainty in WCSM-simulated backscatters originating from different inputs before and after the wheat ear emergence. The results show that despite the presence of wheat ears, uncertainty in root mean square (RMS) height of 0.2 cm significantly influences simulated co-polarized backscatter uncertainty. After ear emergence, uncertainty in ears dominates simulated cross-polarized backscatter uncertainty. In contrast, uncertainty in RMS height before ear emergence dominates the accuracy of simulated cross-polarized backscatter. These findings suggest that considering wheat ears in the model structure and precise representation of surface roughness is essential to accurately simulate backscatter from a wheat field. Since the discrepancy between the simulated and observed backscatter coefficients is due to both model and observation uncertainty, the uncertainty of the UAVSAR data was estimated by analyzing the scatter between multiple backscatter coefficients obtained from the same targets near-simultaneously, assuming the scatter represents the observation uncertainty. Observation uncertainty of UAVSAR backscatter for HH, VV, and HV polarizations are 0.8 dB, 0.87 dB, and 0.86 dB, respectively. Discrepancies between WCSM-simulated backscatter and UAVSAR observations are discussed in terms of simulation and observation uncertainty. Full article

Accurate river hydraulic characterization is fundamental to assess flood risk, parametrize flood forecasting models, and develop river maintenance workflows. River hydraulic roughness and riverbed/floodplain geometry are the main factors controlling inundation extent and water levels. However, gauging stations providing hydrometric observations are declining worldwide, and they provide point measurements only. To describe hydraulic processes, spatially distributed data are required. In situ surveys are costly and time-consuming, and they are sometimes limited by local accessibility conditions. Satellite earth observation (EO) techniques can be used to measure spatially distributed hydrometric variables, reducing the time and cost of traditional surveys. Satellite EO provides high temporal and spatial frequency, but it can only measure large rivers (wider than ca. 50 m) and only provides water surface elevation (WSE), water surface slope (WSS), and surface water width data. UAS hydrometry can provide WSE, WSS, water surface velocity and riverbed geometry at a high spatial resolution, making it suitable for rivers of all sizes. The use of UAS hydrometry can enhance river management, with cost-effective surveys offering large coverage and high-resolution data, which are fundamental in flood risk assessment, especially in areas that difficult to access. In this study, we proposed a combination of UAS hydrometry techniques to fully characterize the hydraulic parameters of a river. The land elevation adjacent to the river channel was measured with LiDAR, the riverbed elevation was measured with a sonar payload, and the WSE was measured with a UAS radar altimetry payload. The survey provided 57 river cross-sections with riverbed elevation, and 8 km of WSE and land elevation and took around 2 days of survey work in the field. Simulated WSE values were compared to radar altimetry observations to fit hydraulic roughness, which cannot be directly observed. The riverbed elevation cross-sections have an average error of 32 cm relative to RTK GNSS ground-truth measurements. This error was a consequence of the dense vegetation on land that prevents the LiDAR signal from reaching the ground and underwater vegetation, which has an impact on the quality of the sonar measurements and could be mitigated by performing surveys during winter, when submerged vegetation is less prevalent. Despite the error of the riverbed elevation cross-sections, the hydraulic model gave good estimates of the WSE, with an RMSE below 3 cm. The estimated roughness is also in good agreement with the values measured at a gauging station, with a Gauckler–Manning–Strickler coefficient of M = 16–17 m^1/3/s. Hydraulic modeling results demonstrate that both bathymetry and roughness measurements are necessary to obtain a unique and robust hydraulic characterization of the river. Full article

Background/Objectives: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) present significant challenges in modern healthcare, leading to increased morbidity, mortality, and healthcare costs. Examination gloves play a critical role in infection prevention by serving as a barrier to reduce the risk of cross-contamination between healthcare workers and patients. This manuscript aims to provide consensus-based guidelines for the optimal selection, use, and disposal of examination gloves in healthcare settings, addressing both infection prevention and environmental sustainability. Methods: The guidelines were developed using a multi-stage Delphi process involving healthcare experts from various disciplines. Recommendations were structured to ensure compliance with international regulations and sustainability frameworks aligned with the One Health approach and Sustainable Development Goals (SDGs). Results: Key recommendations emphasize selecting gloves based on clinical needs and compliance with EN 455 standards. Sterile gloves are recommended for surgical and invasive procedures, while non-sterile gloves are suitable for routine care involving contact with blood and other body fluids or contaminated surfaces. Proper practices include performing hand hygiene before and after glove use, avoiding glove reuse, and training healthcare providers on donning and removal techniques to minimize cross-contamination. Disposal protocols should follow local clinical waste management regulations, promoting sustainability through recyclable or biodegradable materials whenever feasible. Conclusions: These consensus-based guidelines aim to enhance infection control, improve the safety of patients and healthcare workers, and minimize environmental impact. By adhering to these evidence-based practices, grounded in European regulations, healthcare settings can establish safe and sustainable glove management systems that serve as a model for global practices. Full article

This paper presents the design and development project of an engine test stand specifically constructed for ground testing of miniature turbine jet engines (MTJEs) along with conclusive results of the conducted investigations. The tested engines serve as the propulsion system for an unmanned aerial vehicle (UAV) platform. The engine test stand was used to determine various operating parameters of the engine, with a particular focus on recording variations and changes in temperature and pressure at the engine control cross-sections: behind the compressor, the combustion chamber, and at the final cross-section of the nozzle. The analysis of the direct test results allowed the evaluation of the engine’s behavior under hydration conditions and documents the quantitative and qualitative response of the control system of the engine. Of particular interest are the results showing an increase in exhaust system temperature with a decrease in the temperature in combustion chamber under hydrated conditions. The test program assumed and considered the acting loads and forces in both standard and specific flight conditions, including scenarios for a heavy rain. The preliminary evaluation of the investigation results provided data and insights required for further analysis. Quantitatively, the measured temperature value in the exhaust system does not exceed 700 °C and the temperature increase resulting from the introduction of water and the engine’s response to the out-of-operation event is approximately 50 °C for the JetCat 140. Qualitatively different effects were observed in the combustion moment, consisting in a drop in temperature values during the introduction of water into the engine flow channel. The introduction of water into the GTM 140 inlet revealed no significant changes in the variations of pressure and temperature measured in selected engine design sections. Based on the knowledge and experience gained, a fully operational test stand to monitor the parameters and performance of the MTJEs, which are used for aerial target propulsion, was developed. Full article

Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team’s preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation (n = 2261) to develop and test a pipeline for automated hematoma segmentation and to generate ground truth binary HE annotations (≥3, ≥6, ≥9, and ≥12.5 mL). Applying a 95% sensitivity threshold for HE classification showed a practical and efficient strategy for HE annotation in large ICH datasets. This threshold excluded 47–88% of test-negative predictions from expert review of automated segmentations for different HE definitions, with less than 2% false-negative misclassification in both internal and external validation cohorts. Our pipeline offers a time-efficient and optimizable method for generating ground truth HE classifications in large ICH datasets, reducing the burden of expert review of automated hematoma segmentations while minimizing misclassification rate. Full article

The BeiDou Navigation Satellite System (BDS) offers orbit and clock corrections through the B2b signal, enabling Precise Point Positioning (PPP) without relying on ground communication networks. This capability supports applications such as aerial and maritime mapping. However, achieving high precision during the convergence period remains challenging, particularly for missions with short observation durations. To address this, we analyze the performance of PPP over short periods using PPP-B2b products and propose using the backward smoothing method to enhance the accuracy during the convergence period. Evaluation of the accuracy of PPP-B2b products shows that the orbit and clock accuracy of the BDS surpass those of GPS. Specifically, the BDS achieves orbit accuracies of 0.059 m, 0.178 m, and 0.186 m in the radial, along-track, and cross-track components, respectively, with a clock accuracy within 0.13 ns. The hourly static PPP achieves 0.5 m and 0.1 m accuracies with convergence times of 4.5 and 25 min at a 50% proportion, respectively. Nonetheless, 7.07% to 23.79% of sessions fail to converge to 0.1 m due to the limited availability of GPS and BDS corrections at certain stations. Simulated kinematic PPP requires an additional 1–4 min to reach the same accuracy as the static PPP. Using the backward smoothing method significantly enhances accuracy, achieving 0.024 m, 0.046 m, and 0.053 m in the north, east, and up directions, respectively. For vehicle-based positioning, forward PPP can achieve a horizontal accuracy better than 0.5 m within 4 min; however, during the convergence period, positioning errors may exceed 1.5 m and 3.0 m in the east and up direction. By applying the smoothing method, horizontal accuracy can reach better than 0.2 m, while the vertical accuracy can improve to better than 0.3 m. Full article

This study presents analytical solutions grounded in three-dimensional (3D) thermo-elasticity theory to predict the bending behavior of cross-laminated timber (CLT) panels under thermo-mechanical conditions, incorporating the orthotropic and temperature-dependent properties of wood. The model initially utilizes Fourier series expansion based on heat transfer theory to address non-uniform temperature distributions. By restructuring the governing equations into eigenvalue equations, the general solutions for stresses and displacements in the CLT panel are derived, with coefficients determined through the transfer matrix method. A comparative analysis shows that the proposed solution aligns well with finite element results while offering superior computational efficiency. The solution based on the plane section assumption closely matches the proposed solution for thinner panels; however, discrepancies increase as panel thickness rises. Finally, this study explores the thermo-mechanical bending behavior of the CLT panel and proposes a modified superposition principle. The parameter study indicates that the normal stress is mainly affected by modulus and thermal expansion coefficients, while the deflection of the panel is largely dependent on thermal expansion coefficients but less affected by modulus. Full article

Using microwave remote sensing to invert forest parameters requires clear canopy scattering characteristics, which can be intuitively investigated through scattering measurements. However, there are very few ground-based measurements on forest branches, needles, and canopies. In this study, a quantitative analysis of the canopy branches, needles, and ground contribution of Masson pine scenes in C-, X-, and Ku-bands was conducted based on a microwave anechoic chamber measurement platform. Four canopy scenes with different densities by defoliation in the vertical direction were constructed, and the backscattering data for each scene were collected in the C-, X-, and Ku-bands across eight incidence angles and eight azimuth angles, respectively. The results show that in the vertical observation direction, the backscattering energy of the C- and X-bands was predominantly contributed by the ground, whereas the Ku-band signal exhibited higher sensitivity to the canopy structure. The backscattering energy of the scene was influenced by the incident angle, particularly in the cross-polarization, where backscattering energy increased with larger incident angles. The scene’s backscattering energy was influenced by the scattering and extinction of canopy branches and needles, as well as by ground scattering, resulting in a complex relationship with canopy density. In addition, applying orientation correction to the polarization scattering matrix can mitigate the impact of the incident angle and reduce the decomposition energy errors in the Freeman–Durden model. In order to ensure the reliability of forest parameter inversion based on SAR data, a greater emphasis should be placed on physical models that account for signal scattering and the extinction process, rather than relying on empirical models. Full article

The analysis of running gait has conventionally taken place within an expensive and restricted laboratory space, with wearable technology offering a practical, cost-effective, and unobtrusive way to examine running gait in more natural environments. This pilot study presents a wearable inertial measurement unit (IMU) setup for the continuous analysis of running gait during an outdoor parkrun (i.e., 5 km). The study aimed to (1) provide analytical validation of running gait measures compared to time- and age-graded performance and (2) explore performance validation. Ten healthy adults (7 females, 3 males, mean age 37.2 ± 11.7 years) participated. The participants wore Axivity AX6 IMUs on the talus joint of each foot, recording tri-axial accelerometer and gyroscope data at 200 Hz. Temporal gait characteristics—gait cycle, ground contact time, swing time, and duty factor—were extracted using zero-crossing algorithms. The data were analyzed for correlations between the running performance, foot strike type, and fatigue-induced changes in temporal gait characteristics. Strong correlations were found between the performance time and both the gait cycle and ground contact time, with weak correlations for foot strike types. The analysis of asymmetry and fatigue highlighted modest changes in gait as fatigue increased, but no significant gender differences were found. This setup demonstrates potential for in-field gait analysis for running, providing insights for performance and injury prevention strategies. Full article

Parkinson’s disease (PD) is a neurological disorder that severely affects motor function, especially gait, requiring accurate diagnosis and assessment instruments. This study presents Dense Multiscale Sample Entropy (DM-SamEn) as an innovative method for diminishing feature dimensions while maintaining the uniqueness of signal features. DM-SamEn employs a weighting mechanism that considers the dynamic properties of the signal, thereby reducing redundancy and improving the distinctiveness of features extracted from vertical ground reaction force (VGRF) signals in patients with Parkinson’s disease. Subsequent to the extraction process, correlation-based feature selection (CFS) and sequential backward selection (SBS) refine feature sets, improving algorithmic accuracy. To validate the feature extraction and selection stage, three classifiers—Adaptive Weighted K-Nearest Neighbors (AW-KNN), Radial Basis Function Support Vector Machine (RBF-SVM), and Multilayer Perceptron (MLP)—were employed to evaluate classification efficacy and ascertain optimal performance across selection strategies, including CFS, SBS, and the hybrid SBS-CFS approach. K-fold cross-validation was employed to provide improved evaluation of model performance by assessing the model on various data subsets, thereby mitigating the risk of overfitting and augmenting the robustness of the results. As a result, the model demonstrated a significant ability to differentiate between PD patients and healthy controls, with classification accuracy reported as ACC [CI 95%: 97.82–98.5%] for disease identification and ACC [CI 95%: 96.3–97.3%] for severity assessment. Optimal performance was primarily achieved through feature sets chosen using SBS and the integrated SBS-CFS methods. The findings highlight the model’s potential as an effective instrument for diagnosing PD and assessing its severity, contributing to advancements in clinical management of the condition. Full article

Background and Objectives: Thin-section CT (TSCT) is currently the most sensitive imaging modality for detecting bronchiectasis. However, conventional TSCT or HRCT may overlook subtle lung involvement such as alveolar and interstitial changes. Artificial Intelligence (AI)-based analysis offers the potential to identify novel information on lung parenchymal involvement that is not easily detectable with traditional imaging techniques. This study aimed to assess lung involvement in patients with bronchiectasis using the Bronchiectasis Radiologically Indexed CT Score (BRICS) and AI-based quantitative lung texture analysis software (IMBIO, Version 2.2.0). Methods: A cross-sectional study was conducted on 45 subjects diagnosed with bronchiectasis. The BRICS severity score was used to classify the severity of bronchiectasis into four categories: Mild, Moderate, Severe, and tractional bronchiectasis. Lung texture mapping using the IMBIO AI software tool was performed to identify abnormal lung textures, specifically focusing on detecting alveolar and interstitial involvement. Results: Based on the Bronchiectasis Radiologically Indexed CT Score (BRICS), the severity of bronchiectasis was classified as Mild in 4 (8.9%) participants, Moderate in 14 (31.1%), Severe in 11 (24.4%), and tractional in 16 (35.6%). AI-based lung texture analysis using IMBIO identified significant alveolar and interstitial abnormalities, offering insights beyond conventional HRCT findings. This study revealed trends in lung hyperlucency, ground-glass opacity, reticular changes, and honeycombing across severity levels, with advanced disease stages showing more pronounced structural and vascular alterations. Elevated pulmonary vascular volume (PVV) was noted in cases with higher BRICSs, suggesting increased vascular remodeling in severe and tractional types. Conclusions: AI-based lung texture analysis provides valuable insights into lung parenchymal involvement in bronchiectasis that may not be detectable through conventional HRCT. Identifying significant alveolar and interstitial abnormalities underscores the potential impact of AI on improving the understanding of disease pathology and disease progression, and guiding future therapeutic strategies. Full article

Under the action of cross-fault ground motion, bridge piers can experience significant residual displacements, which can irreversibly impact the integrity and reliability of the bridge structure. Traditional seismic mitigation measures struggle to effectively prevent multi-span chain collapses caused by the tilting of bridge piers. Therefore, it is of practical engineering significance to explore the effectiveness of rocking self-centering (RSC) piers as seismic mitigation measures for such bridges. In this paper, cross-fault ground motion with sliding effects is artificially synthesized based on the characteristics of the fault seismogenic mechanism. A finite element model of a cross-fault bridge is established using the OpenSees platform. The applicability of RSC piers to cross-fault bridges is explored. The results show that RSC piers can significantly reduce residual displacement during cross-fault ground motions, facilitating rapid recovery after an earthquake. RSC piers significantly reduce residual displacement in cross-fault bridges, with the most notable vibration reduction effects observed in piers adjacent to the fault. When an 80 cm fault displacement occurs, the vibration reduction rate reaches 48%. Additionally, when the fault’s permanent displacement increases the risk of pier toppling, the vibration reduction effect of the RSC pier is positively correlated with the degree of fault displacement. However, the amplification effect of RSC piers on the maximum relative displacement of bearings in cross-fault bridges cannot be ignored. In this study, for the first time, RSC piers were assembled on bridges spanning faults to investigate their seismic damping effect. When the degree of fault misalignment is greater than 60cm, the seismic damping effect of RSC abutments is positively correlated with the degree of fault misalignment, and its amplifying effect on the maximum relative displacement of the bearing becomes more and more obvious with the increase of permanent displacement. For example, when the fault misalignment degree is 60cm, the vibration reduction rate is 39%, and when the fault misalignment degree is 90cm, the vibration reduction rate is 54%. Designers should rationally configure RSC piers according to the specific bridge and site conditions to achieve optimal vibration reduction effects. Full article

Remote sensing imagery (RSI) segmentation plays a crucial role in environmental monitoring and geospatial analysis. However, in real-world practical applications, the domain shift problem between the source domain and target domain often leads to severe degradation of model performance. Most existing unsupervised domain adaptation methods focus on aligning global-local domain features or category features, neglecting the variations of ground object categories within local scenes. To capture these variations, we propose the scene covariance alignment (SCA) approach to guide the learning of scene-level features in the domain. Specifically, we propose a scene covariance alignment model to address the domain adaptation challenge in RSI segmentation. Unlike traditional global feature alignment methods, SCA incorporates a scene feature pooling (SFP) module and a covariance regularization (CR) mechanism to extract and align scene-level features effectively and focuses on aligning local regions with different scene characteristics between source and target domains. Experiments on both the LoveDA and Yanqing land cover datasets demonstrate that SCA exhibits excellent performance in cross-domain RSI segmentation tasks, particularly outperforming state-of-the-art baselines across various scenarios, including different noise levels, spatial resolutions, and environmental conditions. Full article

Background: Myocardial perfusion imaging (MPI) is a type of single-photon emission computed tomography (SPECT) used to evaluate patients with suspected or confirmed coronary artery disease (CAD). Detection and diagnosis of CAD are complex processes requiring precise and accurate image processing. Proper segmentation is critical for accurate diagnosis, but segmentation issues can pose significant challenges, leading to diagnostic difficulties. Machine learning (ML) algorithms have demonstrated superior performance in addressing segmentation problems. Methods: In this study, a deep learning (DL) algorithm, U-Net, was employed to enhance segmentation accuracy for image segmentation in MPI. Data were collected from 1100 patients who underwent MPI studies at Al-Jahra Hospital between 2015 and 2024. To train the U-Net model, 100 studies were segmented by nuclear medicine (NM) experts to create a ground truth (gold-standard coordinates). The dataset was divided into a training set (n = 100 images) and a validation set (n = 900 images). The performance of the U-Net model was evaluated using multiple cross-validation metrics, including accuracy, precision, intersection over union (IOU), recall, and F1 score. Result: A dataset of 4560 images and corresponding masks was generated. Both holdout and k-fold (k = 5) validation strategies were applied, utilizing cross-entropy and Dice score as evaluation metrics. The best results were achieved with the holdout split and cross-entropy loss function, yielding a test accuracy of 98.9%, a test IOU of 89.6%, and a test Dice coefficient of 94%. The k-fold validation scenario provided a more balanced true positive and false positive rate. The U-Net segmentation results were comparable to those produced by expert nuclear medicine technologists, with no significant difference (p = 0.1). Conclusions: The findings demonstrate that the U-Net model effectively addresses some segmentation challenges in MPI, facilitating improved diagnosis and analysis of mega data. Full article