Search Results (8,622)

Neuroblastoma is a common childhood malignancy, and high-risk presentations, including an MYCN amplified status, continue to result in poor survival. Difluoromethylornithine (DFMO) is a new and well-tolerated treatment for high-risk neuroblastoma. This review article discusses preclinical and clinical data that resulted in the establishment of DFMO as a treatment for neuroblastoma. The review of preclinical data includes a summary of the contribution of polyamine synthetic pathways to high-risk neuroblastoma, the effect that MYCN has on polyamine synthetic pathways, and the proposed mechanism by which DFMO inhibits tumorigenesis. This understanding has led to the discussion of various preclinical combination therapies that may result in a synergistic therapeutic response for high-risk neuroblastoma. We review the clinical trials that show the successful treatment of high-risk neuroblastoma with DFMO, including comparative analysis and traditional neuroblastoma trials using propensity score matching. We review the regulatory path by which DFMO gained approval from the Federal Drug Administration for use as a maintenance therapy following the traditional high-risk neuroblastoma therapy. Finally, we discuss the role of DFMO in future clinical research for neuroblastoma and additional pediatric cancers. Full article

Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. However, polarization SAR methods for ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors face sea clutter modeling and parameter estimation problems in ship detection, which is difficult to adapt to the complex background. In addition, neural network-based detection methods mostly rely on single polarimetric-channel scattering information and fail to fully explore the polarization properties and physical scattering laws of ships. To address these issues, this study constructed two novel characteristics: a helix-scattering enhanced (HSE) local component and a multi-scattering intensity difference (MSID) edge component, which are specifically designed to describe ship scattering characteristics. Based on the characteristic differences of different scattering components in ships, this paper designs a context aggregation network enhanced by local and edge component characteristics to fully utilize the scattering information of polarized SAR data. With the powerful feature extraction capability of a convolutional neural network, the proposed method can significantly enhance the distinction between ships and the sea. Further analysis shows that HSE is able to capture structural information about the target, MSID can increase ship–sea separation capability, and an HV channel retains more detailed information. Compared with other decomposition models, the proposed characteristic combination model performs well in complex backgrounds and can distinguish ship from sea more effectively. The experimental results show that the proposed method achieves a detection precision of 93.6% and a recall rate of 91.5% on a fully polarized SAR dataset, which are better than other popular network algorithms, verifying the reasonableness and superiority of the method. Full article

This paper presents a novel method for measuring organizational resilience by integrating the Rasch model into the Resilience Analysis Grid (RAG), providing a robust and objective tool for cross-sectional resilience studies. By treating the four cornerstones of resilience as abilities, Rasch’s model allows for an assessment that positions both the difficulty of the items and the organizations’ ability along a common scale. The requirement is the availability of a number of different organizations to be assessed. We employ a dataset generated through an artificial simulation and analyzed in a controlled environment, demonstrating the potential of Rasch-based resilience assessments to provide accurate, comparable, and scalable results in different organizational contexts. The traditional RAG is designed without a normative reference group, which makes it challenging to evaluate its results. The proposed model overcomes this limitation by offering a measurement scale on which different organizations can be placed without the need to use a normative group, facilitating the more consistent and timely monitoring of systems. This novel approach to quantifying resilience potentials highlights the transformative role of digital technologies in improving workplace safety and resilience. It advances resilience engineering and occupational health and safety practices in complex environments like manufacturing and industrial sectors. Full article

Aircraft-missed approaches pose significant safety challenges, particularly under adverse weather conditions like wind shear. This study examines the critical factors influencing wind-shear-related missed approaches at Hong Kong International Airport (HKIA) using Pilot Report (PIREP) data from 2015 to 2023. A Binary Logistic Model (BLM) with L1 (Lasso) and L2 (Ridge) regularization was applied to both balanced and imbalanced datasets, with the balanced dataset created using the Synthetic Minority Oversampling Technique (SMOTE). The performance of the BLM on the balanced data demonstrated a good model fit, with Hosmer–Lemeshow statistics of 5.91 (L1) and 5.90 (L2). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were slightly lower for L1 regularization, at 1528.77 and 1574.35, respectively, compared to 1528.86 and 1574.66 for L2. Cohen’s Kappa values were 0.266 for L1 and 0.253 for L2, reflecting moderate agreement between observed and predicted outcomes and improved performance compared to the imbalanced data. The analysis identified designated-approach runway, aircraft classification, wind shear source, and vertical proximity of wind shear to runway as the most influential factors. Runways 07R and 07C, gust fronts as wind shear sources, and wind shear occurring within 400 ft of the runway posed the highest risk for missed approaches. Narrow-body aircrafts also demonstrated greater susceptibility to turbulence-induced missed approaches. These findings show the importance of addressing these risk factors and enhancing safety protocols for adverse weather conditions. Full article

Flood events are among the most destructive natural catastrophes worldwide and pose serious threats to socioeconomic systems, ecological environments, and the safety of human life and property. With the advancement of remote sensing technology, synthetic aperture radar (SAR) has provided new means for flood monitoring. However, traditional methods have limitations when dealing with high noise levels and complex terrain backgrounds. To address this issue, in this study, we adopt an improved U-Net model incorporating the Kolmogorov–Arnold Network (KAN), referred to as UKAN, for the efficient extraction of flood inundation extents from multisource remote sensing data. UKAN integrates the efficient nonlinear mapping capabilities of KAN layers with the multiscale feature fusion mechanism of U-Net, enabling better capturing of complex nonlinear relationships and global features. Experiments were conducted on the C2S-MS Floods and MMFlood datasets, and the results indicate that the UKAN model outperforms traditional models in terms of metrics such as the intersection over union (IoU), precision, recall, and F1 score. On the C2S-MS Floods dataset and the MMFlood dataset, UKAN achieves IoUs of 87.95% and 78.31%, respectively, representing improvements of approximately 3.5 and three percentage points, respectively, over those of the traditional U-Net. Moreover, the model has significant advantages in terms of parameter efficiency and computational efficiency. These findings suggest that the UKAN model possesses greater accuracy and robustness in flood inundation area extraction tasks, which is highly important for increasing the monitoring and early warning capabilities of flood disasters. Full article

The development and study of Smart Grid technologies rely heavily on high-fidelity data from Phasor Measurement Units (PMUs). However, the scarcity of real-world PMU data due to privacy, security, and variability issues poses significant challenges to researchers, developers, and related industries. To address these challenges, this article introduces the bases for a digital metrology framework, focusing on a newly designed and developed synthetic PMU data generator, that is both metrologically accurate and easy to adapt to various grid configurations for data generation from point-on-wave (PoW) data. This initial phase for a Smart Grid research framework aligns with Open Science principles, ensuring that the generated data are Findable, Accessible, Interoperable, and Reusable (FAIR). By embracing these principles, the generated synthetic data not only facilitate collaboration for Smart Grid research but also ensure their easy integration into existing Smart Grid simulation environments. Additionally, the proposed digital metrology framework for Smart Grid research will provide a robust platform for simulating real-world scenarios, such as grid stability, fault detection, and optimization. Through this open science approach, future digital metrology frameworks can support the acceleration of research and development, overcoming current limitations, e.g., lack of significant amounts of real-world scenarios by PMU data. This article also presents an initial case study for situational awareness and control systems, demonstrating the potential for future Smart Grid research framework and its direct real-world impact. All research outcomes are provided to highlight future opportunities for reusability and collaborations by a novel approach for research on sensor network metrology. Full article

Investigating causal interactions between entities is a crucial task across various scientific domains. The traditional causal discovery methods often assume a predetermined causal direction, which is problematic when prior knowledge is insufficient. Identifying causal directions from observational data remains a key challenge. Causal discovery typically relies on two priors: the uniform prior and the Solomonoff prior. The Solomonoff prior theoretically outperforms the uniform prior in determining causal directions in bivariate scenarios by using the causal independence mechanism assumption. However, this approach has two main issues: it assumes that no unobserved variables affect the outcome, leading to method failure if violated, and it relies on the uncomputable Kolmogorov complexity (KC). In addition, we employ Kolmogorov’s structure function to analyze the use of the minimum description length (MDL) as an approximation for KC, which shows that the function class used for computing the MDL introduces prior biases, increasing the risk of misclassification. Inspired by the insufficient but necessary part of an unnecessary but sufficient condition (INUS condition), we propose an asymmetry where the expected complexity change in the cause, due to changes in the effect, is greater than the reverse. This criterion supplements the causal independence mechanism when its restrictive conditions are not met under the Solomonoff prior. To mitigate prior bias and reduce misclassification risk, we introduce a multilayer perceptron based on the universal approximation theorem as the backbone network, enhancing method stability. Our approach demonstrates a competitive performance against the SOTA methods on the TCEP real dataset. Additionally, the results on synthetic datasets show that our method maintains stability across various data generation mechanisms and noise distributions. This work advances causal direction determination research by addressing the limitations of the existing methods and offering a more robust and stable approach. Full article

Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into “essentially stable”, “marginally stable”, “unstable”, and “rapidly deforming” categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters. Full article

The ability to preemptively identify potential failures in industrial parts is crucial for minimizing downtime, reducing maintenance costs and ensuring system reliability and safety. However, challenges such as data nonlinearity, temporal dependencies, and imbalanced datasets complicate accurate fault prediction. In this study, we propose a novel combined approach that integrates the Logistic Model Tree Forest (LMT) with Stacked Long Short-Term Memory (LSTM) networks, addressing these challenges effectively. This hybrid method leverages the decision-making capability of the LMT and the temporal sequence learning ability of Stacked LSTM to improve fault prediction accuracy. Additionally, to tackle the issues posed by imbalanced datasets and noise, we employ the ENN-SMOTE (Edited Nearest Neighbors-Synthetic Minority Over-sampling Technique), a technique for data preprocessing, which enhances data balance and quality. Experimental results show that our approach significantly outperforms traditional methods, achieving a fault prediction accuracy of up to 98.2%. This improvement not only demonstrates the effectiveness of the combined model but also highlights its potential for real-world industrial applications, where high accuracy and reliability are paramount. Full article

The quantization process of synthetic aperture radar (SAR) images faces significant challenges due to their high dynamic range, resulting in notable quantization distortion. This not only degrades the visual quality of the quantized images but also severely impacts the accuracy of image interpretation. To mitigate the distortion caused by uniform quantization and enhance visual quality, this paper introduced a novel nonlinear quantization framework via signal-to-noise ratio (SNR) enhancement and segmentation strategy guidance. This framework introduces guiding information to improve quantization performance in weak scattering regions. A histogram adjustment method is developed to incorporate the spatial information of SAR images into the quantization process to enhance the quantization performance, specifically within weak scattering regions. Additionally, the optimal quantizer is improved by refining the SNR distribution across quantization units, addressing imbalances in their allocation. Experimental results based on Gaofen-3 (GF-3) satellite data demonstrate that the proposed algorithm approaches the global quantization performance of optimal quantizers while achieving superior local quantization performance compared to existing methods. Full article

In dual-channel synthetic aperture radar (SAR) systems, the estimation of the four-dimensional motion parameters of the ground maneuvering target is a critical challenge. In particular, when spatial degrees of freedom are used to enhance the target’s output signal-to-clutter-plus-noise ratio (SCNR), it is possible to have multiple solutions in the parameter estimation of the target. To deal with this issue, a novel algorithm for estimating the motion parameters of ground moving targets in dual-channel SAR systems is proposed in this paper. First, the random sample consensus (RANSAC) and modified adaptive 2D calibration (MA2DC) are used to prevent the target’s phase from being distorted as a result of channel balancing. To address range migration, the RFRT algorithm is introduced to achieve arbitrary-order range migration correction for moving targets, and the generalized scaled Fourier transform (GSCFT) algorithm is applied to estimate the polynomial coefficients of the target. Subsequently, we propose using the synthetic aperture length (SAL) of the target as an independent equation to solve for the four-dimensional parameter information and introduce a windowed maximum SNR method to estimate the SAL. Finally, a closed-form solution for the four-dimensional parameters of ground maneuvering targets is derived. Simulations and real data validate the effectiveness of the proposed algorithm. Full article

The Aburrá Valley, located in the northwestern region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography and dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning and sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics. The kurtosis calculations highlighted areas where infrequent but significant changes occurred, such as large-scale construction projects or sudden shifts in land use, providing a statistical measure of surface variability throughout the study period. An advanced clustering technique segmented images into distinctive classes, utilizing fuzzy logic and a kernel-based method, enhancing the analysis of changes. Additionally, Pearson correlation coefficients were calculated to explore the relationships between identified land cover change classes and their spatial distribution across nine distinct geographic zones in the Aburrá Valley. The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management and environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting and understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation. Full article

Estimating the Probability Density Function (PDF) of observed data is crucial as a problem in its own right, and also for diverse engineering applications. This paper utilizes two powerful mathematical tools, the concept of moments and the relatively little-known Padé approximation to achieve this. On the one hand, moments encapsulate crucial information that is central to both the “time-” and “frequency-”domain representations of the data. On the other hand, the Padé approximation provides an effective means of obtaining a convergent series from the data. In this paper, we invoke these established tools to estimate the PDF. As far as we know, the theoretical results that we have proven, and the experimental results that confirm them, are novel and rather pioneering. The method we propose is nonparametric. It leverages the concept of using the moments of the sample data—drawn from the unknown PDF that we aim to estimate—to reconstruct the original PDF. This is achieved through the application of the Padé approximation. Apart from the theoretical analysis, we have also experimentally evaluated the validity and efficiency of our scheme. The Padé approximation is asymmetric. The most unique facet of our work is that we have utilized this asymmetry to our advantage by working with two mirrored versions of the data to obtain two different versions of the PDF. We have then effectively “superimposed” them to yield the final composite PDF. We are not aware of any other research that utilizes such a composite strategy, in any signal processing domain. To evaluate the performance of the proposed method, we have employed synthetic samples obtained from various well-known distributions, including mixture densities. The accuracy of the proposed method has also been compared with that gleaned by several State-Of-The-Art (SOTA) approaches. The results that we have obtained underscore the robustness and effectiveness of our method, particularly in scenarios where the sample sizes are considerably reduced. Thus, this research confirms how the SOTA of estimating nonparametric PDFs can be enhanced by the Padé approximation, offering notable advantages over existing methods in terms of accuracy when faced with limited data. Full article

Monitoring soil moisture (SM) on individual crop fields is of great interest for agricultural applications. Synthetic aperture radar (SAR) systems such as Sentinel-1 provide sensitivity to surface SM at a spatial resolution compatible with crop-field monitoring. Different algorithms have been proposed to relate SAR backscattering to SM, yet most overlook soil texture as a modulating factor. This study investigated the influence of soil texture, closely related to soil hydrological properties, on the relationship between Sentinel-1 C-band backscattering and surface SM using extensive data from the agricultural sites of the COSMOS-UK monitoring network. Our results evidenced the semi-empirical first-order relationship between SM and field-averaged VV backscattering, and found that the gradient of their linear regression was indicative of soil texture. For instance, in sandy loam soil the S1 response showed high sensitivity to SM with a change of 1.69% SM per dB; this compared with the lower sensitivity of a clayey soil at a change of 4.81% SM per dB. These findings lay the ground for the retrieval of field-scale soil hydrological properties from backscatter temporal patterns, when used in synergy with rainfall data and process-based soil-moisture models. Full article

This study addresses the issue of atmospheric delay correction for the rational polynomial coefficient (RPC) model associated with spaceborne synthetic aperture radar (SAR) imagery under conditions lacking ephemeris data, proposing a novel approach to enhance the geometric positioning accuracy of RPC models. A satellite position inversion method based on the vector-autonomous intersection technique was developed, incorporating ionospheric delay and neutral atmospheric delay models to derive atmospheric delay errors. Additionally, an RPC model reconstruction approach, which integrates atmospheric correction, is proposed. Validation experiments using GF-3 satellite imagery demonstrated that the atmospheric delay values obtained by this method differed by only 0.0001 m from those derived using the traditional ephemeris-based approach, a negligible difference. The method also exhibited high robustness in long-strip imagery. The reconstructed RPC parameters improved image-space accuracy by 18–44% and object-space accuracy by 19–32%. The results indicate that this approach can fully replace traditional ephemeris-based methods for atmospheric delay extraction under ephemeris-free conditions, significantly enhancing the geometric positioning accuracy of SAR imagery RPC models, with substantial application value and development potential. Full article