Search Results (52)

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

Objective: The objective of this study is to develop a convolutional neural network (CNN) for the automatic detection of soft and hard tissue landmarks and the classification of lip thickness on lateral cephalometric radiographs. Methods: A dataset of 1019 pre-orthodontic lateral cephalograms from patients with diverse malocclusions was utilized. A CNN-based model was trained to automatically detect 22 cephalometric landmarks. Upper and lower lip thicknesses were measured using some of these landmarks, and a pre-trained decision tree model was employed to classify lip thickness into the thin, normal, and thick categories. Results: The mean radial error (MRE) for detecting 22 landmarks was 0.97 ± 0.52 mm. Successful detection rates (SDRs) at threshold distances of 1.00, 1.50, 2.00, 2.50, 3.00, and 4.00 mm were 72.26%, 89.59%, 95.41%, 97.66%, 98.98%, and 99.47%, respectively. For nine soft tissue landmarks, the MRE was 1.08 ± 0.87 mm. Lip thickness classification accuracy was 0.91 ± 0.04 (upper lip) and 0.90 ± 0.04 (lower lip) in females and 0.92 ± 0.03 (upper lip) and 0.88 ± 0.05 (lower lip) in males. The area under the curve (AUC) values for lip thickness were ≥0.97 for all gender–lip combinations. Conclusions: The CNN-based landmark detection model demonstrated high precision, enabling reliable automatic classification of lip thickness using cephalometric radiographs. Full article

Background: Understanding the mechanisms by which woody perennials adapt to extreme water deficits is important in regions experiencing increasingly frequent and intense droughts. Methods: We investigated the effects of drought severity in the shrubs Corylus avellana L., C. maxima Mill., and their morphological intermediate forms, all from local Belgian origin, and C. avellana from a Spanish-Pyrenean origin. Potted saplings in a common garden were not receiving any water for a duration of 30 days in July 2021 and developed a range of visual stress symptoms. We assessed responses across the various symptom categories. Results: Droughted plants senesced later than the controls (up to 6 days). The most severely affected plants disproportionately displayed the longest delay (21 days). The delayed leaf senescence was reflected in the subsequent bud burst which was delayed for the droughted plants, with again the largest delay observed for the most severely affected plants. Interestingly, radial growth shifted towards the autumn among the drought-treated plants, suggesting compensation growth after growing conditions normalized. The Spanish-Pyrenean provenance, characterized by smaller plants with smaller leaves, developed visual drought symptoms later than the local provenance during the drought. Conclusions: The results indicate that severe early summer drought, followed by rewatering, not only diminishes radial growth but also prolongs the growth period, and delays leaf senescence. A prolonged time frame for radial growth and a delayed leaf senescence indicate a longer period in which carbon is incorporated in woody tissue or in non-structural carbohydrates. This can help the fine tuning of carbon sequestration modeling. The Pyrenean provenance, adapted to high altitude, holds an advantage under water-limited conditions. Full article

The Soil Moisture Active Passive (SMAP) satellite can measure sea surface winds under tropical cyclone (TC) conditions with its L-band microwave radiometer, without being affected by rainfall or signal saturation. Through the statistical analysis of SMAP data, this study aims to develop radial wind profile models for the TC outer area whose distance from TC center is larger than the radius of maximum wind ($R_{\mathrm{m}}$). A total of 196 TC cases observed by SMAP were collected between 2015 and 2020, and their intensities range from tropical storm to category 5. Based on the wind and radius data, the key model parameters $\alpha$ and $\beta$ were fitted through the Rankine vortex model and the tangential wind profile (TWP) Gaussian model, respectively. $\alpha$ and $\beta$ control the rate of change of the tangential wind speed with radius. Subsequently, for the parametric representation of $\alpha$ and $\beta$, we extracted some TC wind filed parameters, such as maximum wind speed ($U_{\mathrm{m}}$), $R_{\mathrm{m}}$, the average wind speed at $R_{\mathrm{m}}$ ($U_{\mathrm{ma}}$), and the average radius of 17 m/s ($R_{17}$) and examined the relationship between $U_{\mathrm{ma}}$ and $U_{\mathrm{m}}$, the relationship between $R_{\mathrm{m}}$ and $R_{17}$, the relationship between $\alpha$, $U_{\mathrm{m}}$ and $R_{\mathrm{m}}$, and the relationship between $\beta$, $U_{\mathrm{m}}$ and $R_{\mathrm{m}}$. According to the results, the new radial wind profile models were proposed, i.e., SMAP Rankine Model-4 (SRM-4), SMAP Rankine Model-5 (SRM-5), and SMAP Gaussian Model-1 (SGM-1). A significant advantage of these models is that they can simulate average wind distribution through the conversion from $U_{\mathrm{m}}$ to $U_{\mathrm{ma}}$. Finally, comparisons were made between the new models and existing SRM-1, SRM-2, and SRM-3, according to the Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements of 126 TC cases. The results demonstrate that the SRM-4 simulated the radial wind profile best overall, with the lowest root mean-square error (RMSE) of 5.57 m/s, due to replacing the parameter $U_{\mathrm{m}}$ with $U_{\mathrm{ma}}$, using Rankine vortex for α parameterization and modeling with adequate data. Moreover, the models outperform in the Atlantic Ocean, with a RMSE of 5.37 m/s. The new models have the potential to make a contribution to the study of ocean surface dynamics and be used for forcing numerical models under TC conditions. Full article

Traditional classifications of global development, such as the developed/developing dichotomy or Global North/South, often oversimplify the intricate landscape of human development. This paper leverages computational tools, advanced visualization techniques, and mathematical modeling to challenge these conventional categories and reveal a continuous development spectrum among nations. By applying hierarchical clustering, multidimensional scaling, and interactive visualizations to Human Development Index (HDI) data, we identify “development neighborhoods”—clusters of countries that exhibit similar development patterns, sometimes across geographical boundaries. Our methodology combines network theory, statistical physics, and digital humanities approaches to model development as a continuous field, introducing novel metrics for development potential and regional inequality. Through analysis of HDI data from 193 countries (1990–2022), we demonstrate significant regional variations in development trajectories, with Africa showing the highest mean change rate (28.36%) despite maintaining the lowest mean HDI (0.557). The implementation of circle packing and radial dendrogram visualizations reveals both population dynamics and development continuums, while our mathematical framework provides rigorous quantification of development distances and cluster stability. This approach not only uncovers sophisticated developmental progressions but also emphasizes the importance of continuous frameworks over categorical divisions. The findings highlight how digital humanities tools can enhance our understanding of global development, providing policymakers with insights that traditional methods might overlook. Our methodology demonstrates the potential of computational social science to offer more granular analyses of development, supporting policies that recognize the diversity within regional and developmental clusters, while our mathematical framework provides a foundation for future quantitative studies in development economics. Full article

With the intensification of climate change and urbanization, the impact of high-temperature disasters on urban resilience has become increasingly significant. Based on the “Pressure-State-Response” (PSR) model, this study proposes a novel assessment method for urban high-temperature disaster resilience. Through 15 evaluation indicators across 3 categories, we quantified the high-temperature disaster resilience level in Hangzhou and constructed a SOM-K-means second-order clustering algorithm to classify the study area into different resilience zones, exploring the spatial differentiation characteristics of high-temperature disaster resilience. The research results indicate the following: (1) Hangzhou exhibits a relatively low level of high-temperature disaster resilience, with a spatial distribution pattern showing a radial decrease from the main city area at the center, followed by a slight increase in the far periphery of the main city area. (2) The study area was divided into four distinct high-temperature disaster resilience zones, demonstrating significant spatial differentiation characteristics. This study innovatively integrates the PSR model with the SOM-K-means clustering method, providing a new perspective for the quantitative assessment and spatial zoning of urban high-temperature disaster resilience. The findings offer valuable decision-making support for enhancing urban resilience. Full article

The fungal genus Omphalotus is noted for its bioluminescence and the production of biologically active secondary metabolites. We isolated 47 fungal strains of Omphalotus olivascens germinated from spores of a single mushroom. We first noted a high degree of variation in the outward appearances in radial growth and pigmentation among the cultures. Radial growth rates fell into at least five distinct categories, with only slower-growing isolates obtained compared with the parental dikaryon. Scanning UV-vis spectroscopy of liquid-grown cultures showed variation in pigmentation in both the absorption intensity and peak absorption wavelengths, indicating that some isolates vary from the parental strain in both pigment concentration and composition. Bioluminescence intensity was observed to have isolates with both greater and lesser intensities, while the increased emission in response to caffeic acid was inversely proportional to the unstimulated output. Under UV illumination, the media of the parental strain was observed to be brightly fluorescent, which was not due to the pigment, while the isolates also varied from greater to lesser intensity and in their peak emission. At least three separate fluorescent bands were observed by gel electrophoresis from one of the cultures, while only one was observed in others. In a subset of the cultures, fluorescence intensity varied significantly in response to casamino acids. None of this subset produced an antibiotic effective against Staphylococcus aureus, and only the haploids, but not the parental heterokaryon, produced an antibiotic consistent with illudin M effective against Mycobacterium smegmatis. This same subset produced an anticancer agent that was highly potent against MDA-MB-468 breast cancer tumor cells. We interpret these variations in haploids as significant in altering Omphalotus physiology and its production of secondary metabolites, which may in turn alter their ecology and life cycle, and could be further applied to studying fungal physiologies and facilitate linking them to their genetic underpinnings. Full article

This paper is aimed to address the issue of decreased accuracy in the ship block docking caused by the structural errors of posture adjustment mechanism. First, inverse kinematic analysis is performed to investigate the sources of static errors in the mechanism. Subsequently, based on the closed-loop vector method, a pose error model for the moving platform is established, which includes eight categories of error terms. The impact of various structural errors on the pose accuracy of the moving platform is then compared and analyzed under both single-limb and multi-limb configurations. Therefore, a compensation method based on the whale optimization algorithm optimized radial basis function neural network is proposed. By transforming pose errors into actuator length errors, it establishes a predictive model between the theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters, it yields the actuator length compensation to correct the actual pose of the dynamic platform. Simulation and experimental results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. The mean pose accuracy of the moving platform is improved by 85.07%, demonstrating a significant compensation effect. Full article

Information pertaining to a reservoir’s bathymetry is of utmost significance for water resource sustainability and management. The current study evaluated and compensated the reservoir’s bathymetric patterns established using radial basis function (RBF) approaches. Water depth data were acquired by conventionally rolling out a measuring tape into the water. The water depth data were split into three (3) categories, i.e., training data, validation data, and test dataset. Spatial variations in the field-measured bathymetry were determined through descriptive statistics. The thin-plate spline (TPS), multiquadric function (MQF), inverse multiquadric (IMQF), and Gaussian function (GF) were integrated into RBF to establish bathymetric patterns based on the training data. Spatial variations in bathymetry were assessed using Levene’s k-comparison of equal variance. The coefficient of determination ($R^2$), root mean square error (RMSE) and absolute error of mean (AEM) techniques were used to evaluate the uncertainties in the interpolated bathymetric patterns. The regression of the observed estimated (ROE) was used to compensate for uncertainties in the established bathymetric patterns. The Levene’s k-comparison of equal variance technique revealed variations in the predicted bathymetry, with the standard deviation of 8.94, 6.86, 4.36, and 9.65 for RBF with thin-plate spline, multi quadric function, inverse multiquadric function, and Gaussian function, respectively. The bathymetric patterns predicted with thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian function revealed varying accuracy, with AEM values of −1.59, −2.7, 2.87, and −0.99, respectively, $R^2$ values of 0.68, 0.62, 0.50, and 0.70, respectively, and RMSE values of 4.15, 5.41, 5.80 and 3.38, respectively. The compensated mean bathymetric values for thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian-based RBF were noted to be 18.21, 17.82, 17.35, and 18.95, respectively. The study emphasized the ongoing contribution of geospatial technology towards inland water resource monitoring. Full article

Train fault detection often relies on comparing collected images with reference images, making accurate image type recognition crucial. Current systems use Automatic Equipment Identification (AEI) devices to recognize carriage numbers while capturing images, but damaged Radio Frequency (RF) tags or blurred characters can hinder this process. Carriage linear array images, with their high resolution, extreme aspect ratios, and local nonlinear distortions, present challenges for recognition algorithms. This paper proposes a method tailored for recognizing such images. We apply an object detection algorithm to locate key components, simplifying image recognition into a sparse point set alignment task. To handle local distortions, we introduce a weighted radial basis function (RBF) and maximize the similarity between Gaussian mixtures of point sets to determine RBF weights. Experiments show 100% recognition accuracy under nonlinear distortions up to 15%. The algorithm also performs robustly with detection errors and identifies categories from 79 image classes in 24 ms on an i7 CPU without GPU support. This method significantly reduces system costs and advances automatic exterior fault detection for trains. Full article

Transformers are fundamental and among the most expensive electrical devices in any power transmission and distribution system. Therefore, it is essential to implement powerful maintenance methods to monitor and predict their condition. Due to its many advantages—such as early detection, accurate diagnosis, cost reduction, and rapid response time—dissolved gas analysis (DGA) is regarded as one of the most effective ways to assess a transformer’s condition. In this contribution, we propose a new probabilistic hierarchical intelligent system consisting of five subnetworks of the radial basis functions (RBF) type. Indeed, hierarchical classification minimizes the complexity of the discrimination task by employing a divide-and-conquer strategy, effectively addressing the issue of unbalanced data (a significant disparity between the categories to be predicted). This approach contributes to a more precise and sophisticated diagnosis of transformers. The first subnetwork detects the presence or absence of defects, separating defective samples from healthy ones. The second subnetwork further classifies the defective samples into three categories: electrical, thermal, and cellulosic decomposition. The samples in these categories are then precisely assigned to their respective subcategories by the third, fourth, and fifth subnetworks. To optimize the hyperparameters of the five models, the Linde–Buzo–Gray algorithm is implemented to reduce the number of centers (radial functions) in each subnetwork. Subsequently, a single-layer perceptron is trained to determine the optimal synaptic weights, which connect the intermediate layer to the output layer. The results obtained with our proposed system surpass those achieved with another implemented alternative (a single RBF), with an average sensitivity percentage as high as 96.85%. This superiority is validated by a Student’s t-test, showing a significant difference greater than 5% (p-value < 0.001). These findings demonstrate and highlight the relevance of the proposed hierarchical configuration. Full article

The increase in greenhouse gas emissions, as well as the risk of fossil fuel depletion, has prompted a transition to electric transportation. The European Union aims to substantially reduce pollutant emissions by 2035 through the use of renewable energies. In aviation, this transition is particularly challenging, mainly due to the weight of onboard equipment. Traditional electric motors with radial magnetic flux have been replaced by axial magnetic flux motors with reduced weight and volume, high efficiency, power, and torque. These motors were initially developed for electric vehicles with in-wheel motors but have been adapted for aviation without modifications. Worldwide, there are already companies developing propulsion systems for various aircraft categories using such electric motors. One category of aircraft that could benefit from this electric motor development is traditionally constructed training aircraft with significant remaining flight resource. Electric repowering would allow their continued use for pilot training, preparing them for future electrically powered aircraft. This article presents a study on the feasibility of repowering a classic training aircraft with an electric propulsion system. The possibilities of using either a battery or a hybrid source composed of a battery and a fuel cell as an energy source are explored. The goal is to utilize components already in production to eliminate the research phase for specific aircraft components. Full article

Objectives: Accurate outcome prediction is important for making informed clinical decisions in cancer treatment. In this study, we assessed the feasibility of using changes in radiomic features over time (Delta radiomics: absolute and relative) following chemotherapy, to predict relapse/progression and time to progression (TTP) of primary mediastinal large B-cell lymphoma (PMBCL) patients. Material and Methods: Given the lack of standard staging PET scans until 2011, only 31 out of 103 PMBCL patients in our retrospective study had both pre-treatment and end-of-treatment (EoT) scans. Consequently, our radiomics analysis focused on these 31 patients who underwent [¹⁸F]FDG PET-CT scans before and after R-CHOP chemotherapy. Expert manual lesion segmentation was conducted on their scans for delta radiomics analysis, along with an additional 19 EoT scans, totaling 50 segmented scans for single time point analysis. Radiomics features (on PET and CT), along with maximum and mean standardized uptake values (SUVmax and SUVmean), total metabolic tumor volume (TMTV), tumor dissemination (Dmax), total lesion glycolysis (TLG), and the area under the curve of cumulative standardized uptake value-volume histogram (AUC-CSH) were calculated. We additionally applied longitudinal analysis using radial mean intensity (RIM) changes. For prediction of relapse/progression, we utilized the individual coefficient approximation for risk estimation (ICARE) and machine learning (ML) techniques (K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)) including sequential feature selection (SFS) following correlation analysis for feature selection. For TTP, ICARE and CoxNet approaches were utilized. In all models, we used nested cross-validation (CV) (with 10 outer folds and 5 repetitions, along with 5 inner folds and 20 repetitions) after balancing the dataset using Synthetic Minority Oversampling TEchnique (SMOTE). Results: To predict relapse/progression using Delta radiomics between the baseline (staging) and EoT scans, the best performances in terms of accuracy and F1 score (F1 score is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives) were achieved with ICARE (accuracy = 0.81 ± 0.15, F1 = 0.77 ± 0.18), RF (accuracy = 0.89 ± 0.04, F1 = 0.87 ± 0.04), and LDA (accuracy = 0.89 ± 0.03, F1 = 0.89 ± 0.03), that are higher compared to the predictive power achieved by using only EoT radiomics features. For the second category of our analysis, TTP prediction, the best performer was CoxNet (LASSO feature selection) with c-index = 0.67 ± 0.06 when using baseline + Delta features (inclusion of both baseline and Delta features). The TTP results via Delta radiomics were comparable to the use of radiomics features extracted from EoT scans for TTP analysis (c-index = 0.68 ± 0.09) using CoxNet (with SFS). The performance of Deauville Score (DS) for TTP was c-index = 0.66 ± 0.09 for n = 50 and 0.67 ± 03 for n = 31 cases when using EoT scans with no significant differences compared to the radiomics signature from either EoT scans or baseline + Delta features (p-value> 0.05). Conclusion: This work demonstrates the potential of Delta radiomics and the importance of using EoT scans to predict progression and TTP from PMBCL [¹⁸F]FDG PET-CT scans. Full article

Cover crops play a pivotal role in mitigating climate change by bolstering carbon sequestration through biomass production and soil integration. However, current methods for quantifying cover crop biomass lack spatial precision and objectivity. Thus, our research aimed to devise a remote-sensing-based approach to estimate cover crop biomass across various species and mixtures during fallow periods in France. Leveraging Sentinel-2 optical data and machine learning algorithms, we modeled biomass across 50 fields representative of France’s diverse cropping practices and climate types. Initial tests using traditional empirical relationships between vegetation indices/spectral bands and dry biomass revealed challenges in accurately estimating biomass for mixed cover crop categories due to spectral interference from grasses and weeds, underscoring the complexity of modeling diverse agricultural conditions. To address this challenge, we compared several machine learning algorithms (Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) using spectral bands and vegetation indices from the latest available image before sampling as input. Additionally, we developed an approach that incorporates dense optical time series of Sentinel-2 data, generated using a Radial Basis Function for interpolation. Our findings demonstrated that a Random Forest model trained with dense time series data during the cover crop development period yielded promising results, with an average R-squared (r²) value of 0.75 and root mean square error (RMSE) of 0.73 t·ha⁻¹, surpassing results obtained from methods using single-image snapshots (r² of 0.55). Moreover, our approach exhibited robustness in accounting for factors such as crop species diversity, varied climatic conditions, and the presence of weed vegetation—essential for approximating real-world conditions. Importantly, its applicability extends beyond France, holding potential for global scalability. The availability of data for model calibration across diverse regions and timeframes could facilitate broader application. Full article

Pulse signals carry comprehensive information regarding human cardiovascular physiology and pathology, providing a noninvasive and continuous method to assess cardiovascular health status in blood pressure monitoring. The blood pressure measurement method based on the pulse signal needs to extract the features of the single-cycle pulse signal, while the pulse signal pertains to the weak physiological signal of body surface. The acquisition process is susceptible to various factors leading to abnormal cycles, especially adjacent channel interference, affecting the subsequent feature extraction. To address this problem, this paper conducts an analysis of the formation mechanism of adjacent channel interference and proposes a single-cycle pulse signal recognition algorithm based on a one-dimensional deep convolutional neural network (1D-CNN) model. Radial pulse signals were collected from 150 subjects by pulse bracelet, and a dataset comprising 3446 single-cycle signals was extracted in total after denoising, single-cycle segmentation, and standardized preprocessing. The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification is achieved by evaluating the waveform morphology of the signals within a single cycle. The results show that the overall classification accuracy of the algorithm on the test set is 98.26%, in which the classification accuracy of pulse waves is 99.8%, indicating that it can effectively recognize single-cycle pulse waves, which lays the foundation for subsequent continuous blood pressure measurement. Full article