Search Results (20,002)

Deformable image registration plays a crucial role in medical imaging by aligning anatomical structures across multiple datasets, which is essential for accurate diagnosis and treatment planning. However, existing deep learning-based deformable registration models often face challenges in ensuring anatomical plausibility, leading to unnatural deformations in critical brain structures. This paper proposes a novel framework that uses Bayesian optimization to address these challenges, focusing on registering 3D point clouds that represent brain structures. Our method uses probabilistic modeling to optimize non-rigid transformations, providing smooth and interpretable deformations that align with anatomical constraints. The proposed framework is validated using MRI data from patients diagnosed with hypoxic-ischemic encephalopathy (HIE) due to perinatal asphyxia. These datasets include brain scans taken at multiple time points, enabling the modeling of structural changes over time. By incorporating Bayesian optimization, we enhance the accuracy of the registration process while maintaining anatomical fidelity. Our results demonstrate that the approach provides interpretable, anatomically plausible deformations, outperforming conventional methods in terms of accuracy and reliability. This work offers an improved tool for brain MRI analysis, aiding healthcare professionals in better understanding disease progression and guiding therapeutic interventions. Full article

Chinese pine has been extensively planted in the Loess Plateau, but it faces significant threats from Gansu zokor. Traditional methods for monitoring rodent damage rely on manual surveys to assess damage rates but are time-consuming and often underestimate the actual degree of damage, particularly in mildly affected pines. This study proposes a remote sensing monitoring method that integrates hyperspectral analysis with physiological and biochemical parameter models to enhance the accuracy of rodent damage detection. Using ASD Field Spec 4, we analyzed spectral data from 125 Chinese pine needles, measuring chlorophyll (CHC), carotenoid (CAC), and water content (WAC). Through correlation analysis, we identified sensitive vegetation indices (VIs) and red-edge parameters (REPs) linked to different levels of damage. We report several key results. The 680 nm spectral band is instrumental in monitoring damage, with significant decreases in CHC, CAC, and WAC corresponding to increased damage severity. We identified six VIs and five REPs, which were later predicted using stepwise regression (SR), support vector machine (SVM), and random forest (RF) models. Among all models, the vegetation index-based RF model exhibited the best predictive performance, achieving coefficient of determination (R²) values of 0.988, 0.949, and 0.999 for CHC, CAC, and WAC, with root mean square errors (RMSEs) of 0.115 mg/g, 0.042 mg/g, and 0.007 mg/g, and mean relative errors (MREs) of 8.413%, 9.169%, and 1.678%. This study demonstrates the potential of hyperspectral remote sensing technology for monitoring rodent infestations in Chinese pines, providing a reliable basis for large-scale assessments and effective management strategies for pest control. Full article

Maintaining the integrity of urban road pavements is vital for public safety, transportation efficiency, and economic stability. However, aging infrastructure and limited budgets make it challenging to detect subsurface defects that can lead to pavement collapses. Traditional inspection methods are often inadequate for identifying such underground anomalies. Ground Penetrating Radar (GPR), especially dual-polarized array systems, offers a non-destructive, high-resolution solution for subsurface inspection. Despite its potential, effectively detecting and analyzing areas at risk of collapse in urban pavements remains a challenge. This study employed a dual-polarized array GPR system to inspect road pavements in London. The research involved comprehensive field testing, including data acquisition, signal processing, calibration, background noise removal, and 3D migration for enhanced imaging. Additionally, Short-Fourier Transform Spectrum (SFTS) analysis was applied to detect moisture-related anomalies. The results show that dual-polarized GPR systems effectively detect subsurface issues like voids, cracks, and moisture-induced weaknesses. The ability to capture data in multiple polarizations improves resolution and depth, enabling the identification of collapse-prone areas, particularly in regions with moisture infiltration. This study demonstrates the practical value of dual-polarized GPR technology in urban pavement inspection, offering a reliable tool for early detection of subsurface defects and contributing to the longevity and safety of road infrastructure. Full article

This study introduces a novel seatbelt-integrated, non-invasive, beam-focusing metamaterial sensing system characterized by its thinness and flexibility. The system comprises a flexible transmitarray lens and an FMCW radar sensor, enabling the accurate detection and analysis of seatbelt usage and positioning through human tissue. The metasurface design remains effective even when subjected to different bending angles. Our system closely tracks heart rate and respiration, validated against standard reference methods, highlighting its potential for enhancing in-car healthcare monitoring. Experimental results demonstrate the system’s reliability in monitoring physiological signals within dynamic vehicular environments. Full article

Aim: This study sought to evaluate the effectiveness of lactate/albumin ratio for ICU mortality prediction in a large cohort of patients with severe Coronavirus Disease-2019 (COVID-19) admitted to an intensive care unit (ICU). Methods: This is a single-center retrospective cohort study of prospectively collected data derived from the COVID-19 dataset for all critically ill patients admitted to an academic ICU. Data were used to determine the relation between lactate/albumin ratio and other laboratory parameters measured on the first day of the ICU stay and to evaluate the prognostic performance for ICU mortality prediction. Results: A total of 805 ICU patients were included, and the median age (IQR) was 67 (57–76) years, with 68% being male. ICU mortality was 48%, and the median lactate/albumin ratio was 0.53 (0.39–0.59). A survival analysis showed that patients with higher lactate/albumin ratio values had significantly lower survival rates (Log Rank p < 0.001). A multivariable analysis revealed that the lactate/albumin ratio was an independent risk factor for ICU mortality with a hazard ratio of 1.39 (CI: 1.27–1.52). The lactate/albumin ratio showed a receiver operating characteristics area under the curve (ROC-AUC) value to predict ICU mortality significantly higher than that of lactate alone (0.71 vs. 0.68, DeLong test p < 0.001). The optimal lactate/albumin ratio cut-off for predicting ICU mortality was 0.57, with 63% sensitivity and 73% specificity. A subgroup analysis revealed that the lactate/albumin ratio was significantly associated with mortality across different patient groups, including age and sex categories, and those with or without hypertension and coronary heart disease. Conclusions: Lactate/albumin ratio is a reliable prognostic marker in critically ill COVID-19 patients and could predict ICU mortality more accurately than lactate alone. Full article

Underwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of sensor fusion techniques in underwater SLAM, highlighting the amalgamation of proprioceptive and exteroceptive sensors to improve UUV navigational accuracy and system resilience. Essential sensor applications, including inertial measurement units (IMUs), Doppler velocity logs (DVLs), cameras, sonar, and LiDAR (light detection and ranging), are examined for their contributions to navigation and perception. Fusion methodologies, such as Kalman filters, particle filters, and graph-based SLAM, are evaluated for their benefits, limitations, and computational demands. Additionally, innovative technologies like quantum sensors and AI-driven filtering techniques are examined for their potential to enhance SLAM precision and adaptability. Case studies demonstrate practical applications, analyzing the compromises between accuracy, computational requirements, and adaptability to environmental changes. This paper proceeds to emphasize future directions, stressing the need for advanced filtering and machine learning to address sensor drift, noise, and environmental unpredictability, hence improving autonomous underwater navigation through reliable sensor fusion. Full article

Laminated composite bolted joints are increasingly used in the aerospace field, and their damage and failure behavior has been studied in depth. In view of the complexity and stability requirements of laminated composite bolted structures, accurate prediction of damage evolution and failure behavior is significant to ensure the safety and reliability of the structures. In this paper, a novel asymptotic damage model is developed to predict the damage process and failure behavior of laminated composite bolted joints. In this model, the modified Puck criterion and the maximum shear stress criterion are used for fiber yarns. The parabolic yield criterion is adopted for the matrix, and the fiber fracture, inter-fiber fracture and matrix fracture are considered at the microscopic level. The pull-out strength and progressive failure behavior of countersunk and convex bolted joints structures are predicted by using the proposed model, and the corresponding experimental studies are carried out. The results show that the prediction results are in good agreement with the experimental data, which verifies the reliability of the model. Additionally, the effects of different structural parameters (thickness and aperture) on the progressive damage and failure behavior during pull-out is analyzed by the proposed model, and correction factors of pull-out strength are obtained, which provides a powerful tool for the design, analysis and progression of laminated composite bolted joint structures. Full article

Background: Pulmonary arterial elastance (Ea) is a helpful parameter to predict the risk of acute postoperative right ventricular failure (RVF) after left ventricular assist device (LVAD) implantation. A new method for calculating Ea, obtained by the ratio between transpulmonary gradient and stroke volume (Ea^B), has been proposed as a more accurate measure than the Ea obtained as the ratio between pulmonary artery systolic pressure and stroke volume (Ea^C). However, the role of Ea^B in predicting acute RVF post-LVAD implantation remains unclear. Methods and Results: A total of 35 patients who underwent LVAD implantation from 2018 to 2021 were reviewed in this retrospective analysis. Acute RVF after LVAD implantation occurred in 12 patients (34%): 5 patients with moderate RVF (14% of total) and 7 patients with severe RVF. The Ea^B was not significantly different between the “severe RVF” vs. “not-severe RVF” groups (0.27 ± 0.04 vs 0.23 ± 0.1, p < 0.403). However, the combination of arterial elastance and central venous pressure was significantly different between the “not-severe RVF” group (central venous pressure < 14 mmHg and Ea^C < 0.88 mmHg/mL or Ea^B < 0.24 mmHg/mL; p < 0.005) and the “severe RVF” group (central venous pressure > 14 mmHg and Ea^C > 0.88 mmHg/mL or Ea^B > 0.24 mmHg/mL; p < 0.005). Conclusions: Ea is a reliable parameter of right ventricular afterload and helps discriminate the risk of acute RVF after LVAD implantation. The combined analysis of Ea and central venous pressure can also risk stratify patients undergoing LVAD implantation for the development of RVF. Full article

Accurate estimation of evapotranspiration (ET) is critical for understanding land-atmospheric interactions. Despite the advancement in ET measurement, a single ET estimate still suffers from inherent uncertainties. Data fusion provides a viable option for improving ET estimation by leveraging the strengths of individual ET products, especially the triple collocation (TC) method, which has a prominent advantage in not relying on the availability of “ground truth” data. In this work, we proposed a framework for uncertainty analysis and data fusion based on the extended TC (ETC) and multiple TC (MTC) variants. Three different sources of ET products, i.e., the Global Land Evaporation and Amsterdam Model (GLEAM), the fifth generation of European Reanalysis-Land (ERA5-Land), and the complementary relationship model (CR), were selected as the TC triplet. The analyses were conducted based on different climate zones and land cover types across China. Results show that ETC presents outstanding performance as most areas conform to the zero-error correlations assumption, while nearly half of the areas violate this assumption when using MTC. In addition, the ETC method derives a lower root mean square error (RMSE) and higher correlation coefficient (Corr) than the MTC one over most climate zones and land cover types. Among the ET products, GLEAM performs the best, while CR performs the worst. The merged ET estimates from both ETC and MTC methods are generally superior to the original triplets at the site scale. The findings indicate that the TC-based method could be a reliable tool for uncertainty analysis and data fusion. Full article

The proliferation of Internet of Things (IoT) technology has significantly enhanced smart healthcare systems, enabling the collection and processing of vast healthcare datasets such as electronic medical records (EMRs) and remote health monitoring (RHM) data. However, this rapid expansion has also introduced critical challenges related to data security, privacy, and system reliability. To address these challenges, we propose a retrieval integrity verification and multi-system data interoperability mechanism for a Blockchain Oracle in smart healthcare with IoT Integration (RIVMD-BO). The mechanism uses the cuckoo filter technology to effectively reduce the computational complexity and ensures the authenticity and integrity of data transmission and use through data retrieval integrity verification. The experimental results and security analysis show that the proposed method can improve system performance while ensuring security. Full article

Accurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation within transformers. A series of samples reflecting various aging states was obtained and categorized into six distinct groups. Raman spectroscopy analytical technology was employed to characterize the information indicative of different aging states of the oil-immersed barrier insulation. The raw Raman spectra were processed using asymmetric reweighted penalty least squares to correct baseline shifts, Savitzky–Golay (S-G) smoothing to eliminate fluctuation noise, and principal component analysis (PCA) to reduce data dimensionality by extracting principal components. A support vector machine (SVM) classifier was developed to discriminate between the Raman spectra and category labels. The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). Notably, the grid search method demonstrated high efficiency in identifying the best combination of SVM parameters (c and g). Comparative analyses with varying numbers of principal components in SVM classifiers revealed that incorporating an optimal subset of PCA features achieved the highest classification accuracy of 94.44% for external validation samples, with only eight samples being misclassified into adjacent categories. This study offers technical support and a theoretical foundation for the effective assessment of the aging state of oil-barrier type insulation in transformers, contributing to the advancement of condition monitoring and maintenance strategies in power systems. Full article

Background/Objective: Orbital diseases often require precise measurements of eyeball volume, optic nerve sheath diameter (ONSD), and apex-to-eyeball distance (AED) for accurate diagnosis and treatment planning. This study aims to automate and optimize these measurements using advanced deep learning segmentation techniques on orbital Computed Tomography (CT) scans. Methods: Orbital CT datasets from individuals of various age groups and genders were used, with annotated masks for the eyeball and optic nerve. A 2D attention U-Net architecture was employed for segmentation, enhanced with slice-level information embeddings to improve contextual understanding. After segmentation, the relevant metrics were calculated from the segmented structures and evaluated for clinical applicability. Results: The segmentation model demonstrated varying performance across orbital structures, achieving a Dice score of 0.8466 for the eyeball and 0.6387 for the optic nerve. Consequently, eyeball-related metrics, such as eyeball volume, exhibited high accuracy, with a root mean square error (RMSE) of 1.28–1.90 cm${}^3$ and a mean absolute percentage error (MAPE) of 12–21% across different genders and age groups. In contrast, the lower accuracy of optic nerve segmentation led to less reliable measurements of optic nerve sheath diameter (ONSD) and apex-to-eyeball distance (AED). Additionally, the study analyzed the automatically calculated measurements from various perspectives, revealing key insights and areas for improvement. Results: Despite these challenges, the study highlights the potential of deep learning-based segmentation to automate the assessment of ocular structures, particularly in measuring eyeball volume, while leaving room for further improvement in optic nerve analysis. Full article

Traditional coastal flood risk prediction often overlooks critical geographic features, underscoring the need for accurate risk prediction in coastal cities to ensure resilience. This study enhances the prediction of coastal flood occurrence by utilizing the Geospatial Artificial Intelligence (GeoAI) approach. This approach employed models—random forest (RF), k-nearest neighbor (kNN), and artificial neural networks (ANN)—and compared them to the IPCC risk framework. This study used El Salvador as a demonstration case. The models incorporated seven input variables: extreme sea level, coastline proximity, elevation, slope, mangrove distance, population, and settlement type. With a recall score of 0.67 and precision of 0.86, the RF model outperformed the other models and the IPCC approach, which could avoid imbalanced datasets and standard scaler issues. The RF model improved the reliability of flood risk assessments by reducing false negatives. Based on the RF model output, scenario analysis predicted a significant increase in flood occurrences by 2100, mainly under RCP8.5 with SSP5. The study also highlights that the continuous mangrove along the coastline will reduce coastal flood occurrences. The GeoAI approach results suggest its potential for coastal flood risk management, emphasizing the need to integrate natural defenses, such as mangroves, for coastal resilience. Full article

Spectroscopic analysis was employed to evaluate the quality of three bell pepper varieties within the 350–1150 nm wavelength range. Quality parameters such as firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins were measured. To enhance data reliability, principal component analysis (PCA) was used to identify and remove outliers. Raw spectral data were initially modeled using partial least squares regression (PLSR). To optimize wavelength selection, support vector machines (SVMs) were combined with genetic algorithms (GAs), particle swarm optimization (PSO), ant colony optimization (ACO), and imperial competitive algorithm (ICA). The most effective wavelength selection method was subsequently used for further analysis. Three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural networks (ANNs)—were applied to the selected wavelengths. PLSR analysis of raw data yielded a maximum R² value of 0.98 for red pepper pH, while the lowest R² (0.58) was observed for total phenols in yellow peppers. SVM-PSO was determined to be the optimal wavelength selection algorithm based on ratio of performance to deviation (RPD), root mean square error (RMSE), and correlation values. An average of 15 effective wavelengths were identified using this combined approach. Model performance was evaluated using root mean square error of cross-validation and coefficient of determination (R²). ANN consistently outperformed MLR and PLSR in predicting firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins for all three varieties. R² values for the ANN model ranged from 0.94 to 1.00, demonstrating its superior predictive capability. Based on these results, ANN is recommended as the most suitable method for evaluating the quality parameters of bell peppers using spectroscopic data. Full article

An approach to detecting discontinuities in carbon fiber-reinforced polymers, caused by impact loading followed by compression testing, was developed. An X-ray sensor-based installation was used, while some algorithms were developed to improve the quality of the obtained low-contrast radiographic images with negligible signal-to-noise ratios. For epoxy/AF (#1) composite subjected to a “high-velocity” steel-ball impact with subsequent compression loading, it was not possible to detect discontinuities since the orientation of the extended zone of interlayer delamination was perpendicular to the irradiation axis. After drop-weight impacts with subsequent compression loading of epoxy/CF (#2) and PEEK/CF (#3) composites, the main cracks were formed in their central parts. This area was reliably detected through the improved radiographic images being more contrasted compared to that for composite #3, for which the damaged area was similar in shape but smaller. The phase variation and congruency methods were employed to highlight low-contrast objects in the radiographic images. The phase variation procedure showed higher efficiency in detecting small objects, while phase congruency is preferable for highlighting large objects. To assess the degree of image improvement, several metrics were implemented. In the analysis of the model images, the most indicative was the PSNR parameter (with a S-N ratio greater than the unit), confirming an increase in image contrast and a decrease in noise level. The NIQE and PIQE parameters enabled the correct assessment of image quality even with the S-N ratio being less than a unit. Full article