Search Results (145)

Background/Objectives: Acute lymphoblastic leukemia (ALL) is the most prevalent childhood cancer requiring cytotoxic methotrexate treatment. This always necessitates intravenous administration of rescue therapy consisting of calcium folinate and bicarbonate. Current recommendations advise against mixing these two drugs due to concerns regarding precipitate formation of calcium carbonate (CaCO₃) that could result in catheter and capillary obstruction. These recommendations are based on drug concentrations not clinically relevant in pediatric ALL settings. Our study investigated the effect of clinically relevant calcium folinate–bicarbonate concentrations on the risk of CaCO₃ precipitation. Methods: A theoretical prediction model provided estimates of final mixing concentrations in five scenarios: three simulated pediatric patient models (approx. 1, 9, and 14 years), an undiluted drug mix, and a high-risk control outlier case. Physical compatibility tests were conducted using validated methods for particle detection, complemented by Raman spectroscopy for particle identification. Results: Theoretical predictions suggested CaCO₃ precipitation with elevated bicarbonate concentrations and pH levels. Our simulated patient models and high-risk control outlier case showed that CaCO₃ precipitation may be avoided below certain serum methotrexate concentrations and thereby calcium folinate and bicarbonate concentrations. Physical testing demonstrated particle formation only in the undiluted mix with Raman spectroscopy confirming the finding. Conclusions: Mixing calcium folinate and bicarbonate appears safe under specific methotrexate-directed pediatric ALL treatment conditions. While high bicarbonate concentrations pose precipitation risks, protocol-based dosing regimens mitigate this. Switching to disodium folinate or using in-line filters could further enhance co-administration safety if bicarbonate concentrations exceed the safety limit suggested by our results. Full article

Structural parameters, such as strain or deflection, were collected by sensors and analyzed to assess the bridge’s structural condition and obtain a reliable reference for bridge maintenance. In the data acquisition and transmission process, sensor data inevitably contains noise and interference, resulting in anomalies, especially data distortion during wireless transmission. These anomalies significantly impact data analysis and structural evaluation. To mitigate the effects of these abnormalities, we conducted the cause analysis. The Sanxia Viaduct was used to design a strain monitoring method as a bridge model. We analyzed vibrating string sensor data collected in the cold environment using the Nair method to eliminate outlier data. The analysis results of strain and temperature trends showed that the data fusion method developed in this study showed high precision and stability and effectively reduced the impact of noise and data anomalies. By monitoring actual bridges, the effectiveness and practicality of the method were validated. The model provides significant information on the development and application of bridge health monitoring technology. Full article

Multi-source topographic point clouds are of great value in applications such as mine monitoring, geological hazard assessment, and high-precision terrain modeling. However, challenges such as heterogeneous data sources, drastic terrain variations, and significant differences in point density severely hinder accurate registration. To address these issues, this study proposes a robust point cloud registration method named Cauchy-AdaV2, which integrates region-adaptive weighting with Cauchy-based residual suppression. The method jointly leverages slope and roughness to partition terrain into regions and constructs a spatially heterogeneous weighting function. Meanwhile, the Cauchy M-estimator is employed to mitigate the impact of outlier correspondences, enhancing registration accuracy while maintaining adequate correspondence coverage. The results indicate that the proposed method significantly outperforms traditional ICP, GICP, and NDT methods in terms of overall error metrics (MAE, RMSE), error control in complex terrain regions, and cross-sectional structural alignment. Specifically, it achieves a mean absolute error (MAE) of 0.0646 m and a root mean square error (RMSE) of 0.0688 m, which are 70.5% and 72.4% lower than those of ICP, respectively. These outcomes demonstrate that the proposed method possesses stronger spatial consistency and terrain adaptability. Ablation studies confirm the complementary benefits of regional and residual weighting, while efficiency analysis shows the method to be practically applicable in large-scale point cloud scenarios. This work provides an effective solution for high-precision registration of heterogeneous point clouds, especially in challenging environments characterized by complex terrain and strong disturbances. Full article

Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity. However, traditional NMF often struggles to effectively capture and preserve topological structure information between data during low-dimensional representation. Therefore, this paper proposes an autoencoder-like sparse non-negative matrix factorization with structure relationship preservation (ASNMF-SRP). Firstly, drawing on the principle of autoencoders, a “decoder-encoder” co-optimization matrix factorization framework is constructed to enhance the factorization stability and representation capability of the coefficient matrix. Then, a preference-adjusted random walk strategy is introduced to capture higher-order neighborhood relationships between samples, encoding multi-order topological structure information of the data through an optimal graph regularization term. Simultaneously, to mitigate the impact of noise and outliers, the $l_{2,1}$-norm is used to constrain the feature correlation between low-dimensional representations and the original data, preserving feature relationships between data, and a sparse constraint is imposed on the coefficient matrix via the inner product. Finally, clustering experiments conducted on 8 public datasets demonstrate that ASNMF-SRP consistently exhibits favorable clustering performance. Full article

Precise forecasting of rockburst intensity categories is vital to safeguarding operational safety and refining design protocols in deep underground engineering. This study proposes an intelligent forecasting framework through the integration of k-medoids-SMOTE and the BSLO-optimized Random Forest (BSLO-RF) algorithm. A curated dataset encompassing 351 rockburst instances, stratified into four intensity grades, was compiled via systematic literature synthesis. To mitigate data imbalance and outlier interference, z-score normalization and k-medoids-SMOTE oversampling were implemented, with t-SNE visualization confirming improved inter-class distinguishability. Notably, the BSLO algorithm was utilized for hyperparameter tuning of the Random Forest model, thereby strengthening its global search and local refinement capabilities. Comparative analyses revealed that the optimized BSLO-RF framework outperformed conventional machine learning methods (e.g., BSLO-SVM, BSLO-BP), achieving an average prediction accuracy of 89.16% on the balanced dataset—accompanied by a recall of 87.5% and F1-score of 0.88. It exhibited superior performance in predicting extreme grades: 93.3% accuracy for Level I (no rockburst) and 87.9% for Level IV (severe rockburst), exceeding BSLO-SVM (75.8% for Level IV) and BSLO-BP (72.7% for Level IV). Field validation via the Zhongnanshan Tunnel project further corroborated its reliability, yielding an 80% prediction accuracy (four out of five cases correctly classified) and verifying its adaptability to complex geological settings. This research introduces a robust intelligent classification approach for rockburst intensity, offering actionable insights for risk assessment and mitigation in deep mining and tunneling initiatives. Full article

The changing global climate and rapid urbanization make extreme rainstorm events frequent, and the flood disaster caused by rainstorm has become a prominent problem of urban public safety in China, which severely restricts the healthy and sustainable development of social economy. The weight calculation method of traditional risk assessment model is single and ignores the difference of multi-dimensional information space involved in risk analysis. This study constructs a flood risk assessment model by incorporating natural, social, and economic factors into an indicator system structured around four dimensions: hazard, exposure, vulnerability, and disaster prevention and mitigation capacity. A combination of the Analytic Hierarchy Process (AHP) and the entropy weight method is employed to optimize both subjective and objective weights. Taking the central urban area of Wuhan with a high flood risk as an example, based on the risk assessment values, spatial autocorrelation analysis, cluster analysis, outlier analysis, and hotspot analysis are applied to explore the spatial clustering characteristics of risks. The results show that the overall assessment level of flood hazard in central urban area of Wuhan is medium, the overall assessment level of exposure and vulnerability is low, and the overall disaster prevention and mitigation capability is medium. The overall flood risk levels in Wuchang and Jianghan are the highest, while some areas in Qingshan and Hanyang have the lowest levels. The spatial characteristics of each dimension evaluation index show obvious autocorrelation and spatial differentiation. These findings aim to provide valuable suggestions and references for reducing urban disaster risks and achieving sustainable urban development. Full article

Global Navigation Satellite System (GNSS) receivers may produce measurement outliers in real-world applications owing to various circumstances, including poor signal quality, multipath effects, data loss, satellite signal loss, or electromagnetic interference. This can lead to a noise distribution that is non-Gaussian heavy-tailed, affecting the effectiveness of satellite navigation filters. This paper presents a robust Extended Kalman Filter (EKF) based on the Maximum Correntropy Criterion with a Student’s t kernel (STMCCEKF) for GPS navigation under non-Gaussian noise. Unlike traditional EKF and Gaussian-kernel MCCEKF, the proposed method enhances robustness by leveraging the heavy-tailed Student’s t kernel, which effectively suppresses outliers and dynamic observation noise. A fixed-point iterative algorithm is used for state update, and a new posterior error covariance expression is derived. The simulation results demonstrate that STMCCEKF outperforms conventional filters in positioning accuracy and robustness, particularly in environments with impulsive noise and multipath interference. The Student’s t-distribution kernel efficiently mitigates heavy-tailed non-Gaussian noise, while it adaptively adjusts process and measurement noise covariances, leading to improved estimation performance. A detailed explanation of several key concepts along with practical examples are discussed to aid in understanding and applying the Global Positioning System (GPS) navigation filter. By integrating cutting-edge reinforcement learning with robust statistical approaches, this work advances adaptive signal processing and estimation, offering a significant contribution to the field. Full article

Background: Emerging evidence indicates that some individuals recovering from COVID-19 develop persistent symptoms, including fatigue, pain, cognitive difficulties, and psychological distress, commonly known as Long COVID. These symptoms often overlap with those seen in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME), underscoring the need for integrative, non-pharmacological interventions. This Phase II controlled trial aimed to evaluate the feasibility and preliminary efficacy of Heart Rate Variability Biofeedback (HRV-BF) in individuals with Long COVID who meet the diagnostic criteria for CFS/ME. Specific objectives included assessing feasibility indicators (drop-out rates, side effects, participant satisfaction) and changes in fatigue, depression, anxiety, pain, and health-related quality of life. Methods: Participants were assigned alternately and consecutively to the HRV-BF intervention or Treatment-as-usual (TAU), in a predefined 1:1 sequence (quasirandom assignment). The intervention consisted of 10 HRV-BF sessions, held twice weekly over 5 weeks, with each session including a 10 min respiratory preparation and 40 min of active training. Results: The overall drop-out rate was low (5.56%), and participants reported a generally high level of satisfaction. Regarding side effects, the mean total Simulator Sickness Questionnaire score was 24.31 (SD = 35.42), decreasing to 12.82 (SD = 15.24) after excluding an outlier. A significantly greater improvement in severe fatigue was observed in the experimental group (H = 4.083, p = 0.043). When considering all outcomes collectively, a tendency toward improvement was detected in the experimental group (binomial test, p < 0.0001). Conclusions: HRV-BF appears feasible and well tolerated. Findings support the need for Phase III trials to confirm its potential in mitigating fatigue in Long COVID. Full article

Federated learning (FL) has emerged as a powerful approach for privacy-preserving model training in autonomous vehicle networks, where real-world deployments rely on multiple roadside units (RSUs) serving heterogeneous clients with intermittent connectivity. While most research focuses on single-server or hierarchical cloud-based FL, multi-server FL can alleviate the communication bottlenecks of traditional setups. To this end, we propose an edge-based, multi-server FL (MS-FL) framework that combines performance-driven aggregation at each server—including statistical weighting of peer updates and outlier mitigation—with an application layer handover protocol that preserves model updates when vehicles move between RSU coverage areas. We evaluate MS-FL on both MNIST and GTSRB benchmarks under shard- and Dirichlet-based non-IID splits, comparing it against single-server FL and a two-layer edge-plus-cloud baseline. Over multiple communication rounds, MS-FL with the Statistical Performance-Aware Aggregation method and Dynamic Weighted Averaging Aggregation achieved up to a 20-percentage-point improvement in accuracy and consistent gains in precision, recall, and F1-score (95% confidence), while matching the low latency of edge-only schemes and avoiding the extra model transfer delays of cloud-based aggregation. These results demonstrate that coordinated cooperation among servers based on model quality and seamless handovers can accelerate convergence, mitigate data heterogeneity, and deliver robust, privacy-aware learning in connected vehicle environments. Full article

In brain mapping, structural templates derived from population-specific MRI scans are essential for normalizing individual brains into a common space. This normalization facilitates accurate group comparisons and statistical analyses. Although templates have been developed for various populations, none currently exist for the Saudi population. To our knowledge, this work introduces the first structural brain template constructed and evaluated from a homogeneous subset of T1-weighted MRI scans of 11 healthy Saudi female subjects aged 25 to 30. Our approach combines the symmetric model construction (SMC) method with a covariance-based weighting scheme to mitigate bias caused by over-represented anatomical features. To enhance the quality of the template, we employ a patch-based mean-shift intensity estimation method that improves image sharpness, contrast, and robustness to outliers. Additionally, we implement computational optimizations, including parallelization and vectorized operations, to increase processing efficiency. The resulting template exhibits high image quality, characterized by enhanced sharpness, improved tissue contrast, reduced sensitivity to outliers, and minimized anatomical bias. This Saudi-specific brain template addresses a critical gap in neuroimaging resources and lays a reliable foundation for future studies on brain structure and function in this population. Full article

Coastal cities and low-lying areas are increasingly vulnerable, and accurate data is needed to identify where interventions are most required. We compared 53 cities affected by a 1 m increase in land levels and a 2 m rise in sea levels. The geographical scope of this study covered selected coastal cities in Europe and northern Africa. Data were sourced from the European Environment Agency (EEA) in the form of prepared datasets, which were further processed for analysis. Statistical methods were applied to compare the extent of urban flooding under two sea level rise scenarios—1 m and 2 m—by calculating the percentage of affected urban areas. To assess social vulnerability, the analysis included several variables: MAPF65 (Mean Area Potentially Flooded for people aged 65 and older, indicating elderly exposure), Age (the percentage of the population aged 65+ in each city), MAPF (Mean Area Potentially Flooded, representing the average share of urban area at risk of flooding), and Unemployment Ratio (the percentage of unemployed individuals living in the areas potentially affected by sea level rise). We utilized t-tests to analyze the means of two datasets, yielding a mean difference of 2.9536. Both parametric and bootstrap confidence intervals included zero, and the p-values from the t-tests (0.289 and 0.289) indicated no statistically significant difference between the means. The Bayes factor (0.178) provided substantial evidence supporting equal means, while Cohen’s D (0.099) indicated a very small effect size. Ceuta’s flooding value (502.8) was identified as a significant outlier (p < 0.05), indicating high flood risk. A Grubbs’ test confirmed Ceuta as a significant outlier. A Wilcoxon test highlighted significant deviations between the medians, with a p << 0.001, demonstrating systematic discrepancies tied to flood frequency and sea level anomalies. These findings illuminated critical disparities in flooding trends across specific locations, offering essential insights for urban planning and mitigation strategies in cities vulnerable to rising sea levels and extreme weather patterns. Information on coastal flooding provides awareness of how rising sea levels affect at-risk areas. Examining factors such as MAPF and population data enables the detection of the most threatened zones and supports targeted action. These perceptions are essential for strengthening climate resilience, improving emergency planning, and directing resources where they are needed most. Full article

In smart meters, loose contact at screw terminals can lead to prolonged overheating and arcing, posing significant fire hazards. To mitigate these risks through early fault detection, this study proposes a data-driven framework integrating the Local Outlier Factor (LOF) and Multiple Linear Regression (MLR) algorithms. Voltage differentials, extracted from operational data collected via a simulated multi-meter metering enclosure, are leveraged to diagnose terminal contact degradation. Specifically, LOF identifies arc faults, characterized by abrupt and transient voltage deviations, by detecting outliers in voltage differentials, while MLR quantifies contact resistance through regression analysis, enabling precise loose contact detection, a condition associated with gradual and persistent voltage changes due to increased resistance. Extensive validation demonstrates the framework’s robustness, outperforming conventional centralized methods in diagnostic accuracy and adaptability to diverse load conditions. Full article

To address the limited inversion accuracy caused by low-fidelity data in satellite gravimetry, this study proposes a data preprocessing framework based on iterative residual correction. Utilizing Level-1B observations from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellite (January 2020), outliers were systematically detected and removed, while data gaps were compensated through spline interpolation. Experimental results demonstrate that the proposed method effectively mitigates data discontinuities and anomalous perturbations, achieving a significant improvement in data quality. Furthermore, a 60-order Earth gravity field model derived via the energy balance approach was validated against contemporaneous models published by the University of Texas Center for Space Research (CSR), German Research Centre for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL). The results reveal a two-order-of-magnitude enhancement in inversion precision, with model accuracy improving from 10⁻⁶–10⁻⁷ to 10⁻⁸–10⁻⁹. This method provides a robust solution for enhancing the reliability of gravity field recovery in satellite-based geodetic missions. Full article

Background/Objectives: Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In ⁶⁸Ga-PSMA PET, however, artifacts such as respiratory motion, halo effects, and truncation errors in CT-based AC (CT-AC) images compromise image quality and impair model training for deep learning-based AC. This study proposes a novel artifact-refinement framework that filters out corrupted PET-CT images to create a clean dataset for training an image-domain AC model, eliminating the need for anatomical reference scans. Methods: A residual neural network (ResNet) was trained using paired PET non-AC and PET CT-AC images from a dataset of 828 whole-body ⁶⁸Ga-PSMA PET-CT scans. An initial model was trained using all data and employed to identify artifact-affected samples via voxel-level error metrics. These outliers were excluded, and the refined dataset was used to retrain the model with an L2 loss function. Performance was evaluated using metrics including mean error (ME), mean absolute error (MAE), relative error (RE%), RMSE, and SSIM on both internal and external test datasets. Results: The model trained with the artifact-free dataset demonstrated significantly improved performance: ME = −0.009 ± 0.43 SUV, MAE = 0.09 ± 0.41 SUV, and SSIM = 0.96 ± 0.03. Compared to the model trained on unfiltered data, the purified data model showed enhanced quantitative accuracy and robustness in external validation. Conclusions: The proposed data purification framework significantly enhances the performance of deep learning-based AC for ⁶⁸Ga-PSMA PET by mitigating artifact-induced errors. This approach facilitates reliable PET imaging in the absence of anatomical references, advancing clinical applicability and image fidelity. Full article

Reinforcement learning has achieved significant success in sequential decision-making problems but exhibits poor adaptability in non-stationary environments with unknown dynamics, a challenge particularly pronounced in multi-agent scenarios. This study aims to enhance the adaptive capability of multi-agent systems in such volatile environments. We propose a novel cooperative Multi-Agent Reinforcement Learning (MARL) algorithm based on MADDPG, termed MACPH, which innovatively incorporates three mechanisms: a Composite Experience Replay Buffer (CERB) mechanism that balances recent and important historical experiences through a dual-buffer structure and mixed sampling; an Adaptive Parameter Space Noise (APSN) mechanism that perturbs actor network parameters and dynamically adjusts the perturbation intensity to achieve coherent and state-dependent exploration; and a Huber loss function mechanism to mitigate the impact of outliers in Temporal Difference errors and enhance training stability. The study was conducted in standard and non-stationary navigation and communication task scenarios. Ablation studies confirmed the positive contributions of each component and their synergistic effects. In non-stationary scenarios featuring abrupt environmental changes, experiments demonstrate that MACPH outperforms baseline algorithms such as DDPG, MADDPG, and MATD3 in terms of reward performance, adaptation speed, learning stability, and robustness. The proposed MACPH algorithm offers an effective solution for multi-agent reinforcement learning applications in complex non-stationary environments. Full article