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Found 6342 publications
An Analytically Derived Gauss–Legendre Quadrature for Axis-Aligned Ellipse–Ellipse Intersection
Publication Name: Mathematics
Publication Date: 2025-12-01
Volume: 13
Issue: 23
Page Range: Unknown
Description:
Accurate and efficient evaluation of the intersection area between two axis-aligned ellipses is essential in applications where the coordinate system or underlying geometry naturally imposes alignment. However, most existing numerical integration techniques are designed for arbitrarily oriented ellipses, and their generality typically requires adaptive refinement or solving higher-degree algebraic intersection formulations, leading to greater computational cost than necessary in the axis-aligned case. This study introduces two analytically derived, fixed-cost Gauss–Legendre quadrature formulations for computing the intersection area in the axis-aligned configuration. The first is a sine-mapped Gauss–Legendre quadrature, which applies a trigonometric transformation to improve conditioning near endpoint singularities while retaining constant-time evaluation. The second is an enhanced two-panel affine-normalized formulation, which splits the intersection domain into two sub-intervals to increase local accuracy while maintaining a fixed computational cost. Both methods are benchmarked against adaptive Simpson integration, polygonal discretization, and Monte Carlo sampling over 10,000 randomly generated ellipse pairs. The two-panel formulation achieves a mean relative error of 0.003% with runtimes more than twenty times faster than the adaptive reference and remains consistently more efficient than the polygonal and Monte Carlo approaches while exhibiting comparable or superior numerical behavior across all tested regimes.
Open Access: Yes
DOI: 10.3390/math13233814
Evaluation of early warning signals for soil erosion using remote sensing indices in northeastern Iran
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Soil erosion represents a major challenge to natural resource conservation, causing land degradation, biodiversity loss, and diminished soil quality. This study explored the use of satellite imagery to evaluate the spatiotemporal risk of soil erosion in northeastern Iran. The ICONA model was applied to identify areas at severe erosion risk, while remote sensing indices (NDVI, NDSI, and TGSI) were employed to analyze erosion trends. NDVI is used to monitor vegetation health, NDSI detects soil salinity levels, and TGSI assesses topsoil grain size distribution, collectively providing critical insights into soil erosion risk in the study area. These indices, derived from the Google Earth Engine with a 30-meter spatial resolution and monthly temporal intervals (2003–2022), were assessed at 100 points, equally divided between eroded and non-eroded regions. Field data, including vegetation plots and soil profiles, were used to validate the remote sensing outputs. Early warning signals were analyzed through three statistical indices—autocorrelation coefficient, skewness, and standard deviation—using Kendall’s tau. Results revealed that 39.7% of the area falls under low erosion risk, 58.4% under medium risk, and 1.9% under severe risk. Significant breakpoints in NDSI and NDVI were identified in 2013, while TGSI showed no detectable change. Major shifts occurred near the Alagol, Almagol, and Ajigol wetlands and northern drylands. This study underscores the importance of integrating satellite data with field validation to improve soil management, protect biodiversity, and guide sustainable erosion mitigation strategies.
Open Access: Yes
A novel numerical investigation of fiber Bragg gratings with dispersive reflectivity having polynomial law of nonlinearity
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Fiber Bragg gratings represent a pivotal advancement in the field of photonics and optical fiber technology. The numerical modeling of fiber Bragg gratings is essential for understanding their optical behavior and optimizing their performance for specific applications. In this paper, numerical solutions for the revered optical fiber Bragg gratings that are considered with a cubic-quintic-septic form of nonlinear medium are constructed first time by using an iterative technique named as residual power series technique (RPST) via conformable derivative. The competency of the technique is examined by several numerical examples. By considering the suitable values of parameters, the power series solutions are illustrated by sketching 2D, 3D, and contour profiles. The results obtained by employing the RPST are compared with exact solutions to reveal that the method is easy to implement, straightforward and convenient to handle a wide range of fractional order systems in fiber Bragg gratings. The obtained solutions can provide help to visualize how light propagates or deforms due to dispersion or nonlinearity.
Open Access: Yes
A Disordered Eating Screen For Athletes magyar változata (DESA-6H) konvergens validitásának vizsgálata – Egy pilot study eredményei [Convergent validity of the Hungarian version of the Disordered Eating Screen For Athletes (DESA-6H) – Results of a pilot study]
Publication Name: Mentalhigiene Es Pszichoszomatika
Publication Date: 2025-12-01
Volume: 26
Issue: 4
Page Range: 242-243
Description:
A Disordered Eating Screen For Athletes magyar változata (DESA-6H) konvergens validitásának vizsgálata – Egy pilot study eredményei [Convergent validity of the Hungarian version of the Disordered Eating Screen For Athletes (DESA-6H) – Results of a pilot study] Mentálhigiéné és Pszichoszomatika, 26(3), 123–137. https://doi.org/10.1556/0406.2025.00074 A fenti cikk Mellékletében másolási hiba folytán két kérdés nem megfelelően jelent meg. A 136. oldalon a kérdések felsorolásánál a 2. kérdés két válaszlehetőség sorrendjét felcseréltük.
Open Access: Yes
Web crippling behavior of cold-formed steel built-up I-sections with stiffened and unstiffened perforated webs
Publication Name: Results in Engineering
Publication Date: 2025-12-01
Volume: 28
Issue: Unknown
Page Range: Unknown
Description:
This study investigates the web crippling behaviour of cold-formed steel (CFS) back-to-back built-up U-shaped sections with perforated webs. In this research, the web opening was positioned at the mid-height of the web directly beneath the bearing plate. Initially, the geometrically and materially nonlinear finite element (FE) models were validated against 24 experimental tests from the literature, demonstrating excellent agreement. Specifically, the mean FE-to-experimental strength ratios (PFE /PEXP ) were 1.002 and 1.001 for the Interior-Two-Flange (ITF) and Interior-Loading (IL) conditions, respectively. Subsequently, the verified nonlinear FEM models were employed to conduct an extensive parametric study comprising 198 built-up I-sections. Moreover, this extensive investigation systematically examined the effects of various parameters, including hole size, the presence of unstiffened and stiffened holes, as well as different hole shapes such as rectangular, slotted, circular, and square openings, on web crippling performance. Furthermore, the results indicate that unstiffened holes can reduce the web crippling strength by as much as 54 % compared to plain webs. In contrast, edge-stiffened holes can enhance the web crippling strength by up to 42 % relative to plain webs. These findings highlight the significant impact of web perforation geometry and stiffening on the web-crippling behaviour of CFS built-up sections.
Open Access: Yes
Biogeography-Based Optimization of Machine Learning Models for Accurate Penetration Rate Prediction Using Rock Texture Coefficient
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Predicting drill penetration rate (PR) in rock environments remains a significant challenge due to the complex interplay between rock texture, drilling fluid properties, and operational parameters. Traditional empirical models often lack generalizability and are based on inconsistent datasets, limiting their reliability. To address these limitations, this study develops a comprehensive experimental dataset using rock samples collected from various mines in Iran, tested under controlled laboratory conditions with different drilling fluids, bit loads, and rotational speeds. Texture coefficient (TC), electrical conductivity (EC), load on bit (LOB), and bit rotational velocity (BRV) were selected as input features. Four machine learning models—support vector regression (SVR), stochastic gradient descent (SGD), K-nearest neighbors (KNN), and decision tree (DT)—were trained to predict PR. A biogeography-based optimization (BBO) algorithm was employed to fine-tune hyperparameters and enhance model accuracy. Additionally, a novel hybrid error index (HEI) was introduced to comprehensively evaluate model performance. Among all models, the DT achieved the best accuracy with an HEI of 0.3753, followed by KNN, SVR, and SGD. These findings demonstrate the potential of the DT model, combined with optimized learning and a robust dataset, to reliably predict penetration rate in rock-based engineering projects.
Open Access: Yes
Effect of filament humidity on the properties of material extrusion 3D-printed acrylonitrile butadiene styrene/hexagonal boron nitride composites
Publication Name: Emergent Materials
Publication Date: 2025-12-01
Volume: 8
Issue: 8
Page Range: 6791-6806
Description:
This study investigates the effect of filament moisture content on material extrusion (MEX) 3D-printed composites using acrylonitrile butadiene styrene (ABS) as the polymer matrix and 0–10 vol% hexagonal boron nitride (BN) as reinforcement. ABS/BN composites were prepared through batchwise compounding and extruded into MEX-suitable filaments. The filaments were conditioned at 30 °C and 10% or 90% relative humidity (RH) before/during direct feeding into the 3D printer. Specimens were fabricated with raster angles parallel (0°) and perpendicular (90°) to their length. Micro- and macrostructural analyses using scanning electron microscopy and computed tomography revealed intensive void formation, especially in BN-filled composites 3D-printed from humid filaments. This was attributed to BN acting as a physical barrier, hindering the outgassing of evaporated water during 3D printing. Mechanical properties were evaluated using tensile and Charpy impact tests. Based on the tensile test results, neat ABS was the least sensitive to filament moisture, with tensile strength at 0° raster angle dropping from 40.5 MPa to 36.7 MPa as storage RH was increased from 10 to 90%. For composites with 10 vol% BN loading, tensile strength dropped from 34.1 MPa to 22.3 MPa. Charpy impact strength exhibited similar reductions, ascribed to the porous structure of the BN-filled composites caused by the evaporated moisture. Thermal conductivity was also examined, showing slightly superior performance for samples 3D-printed from filaments stored in less humid conditions. For unfilled ABS, the conductivity slightly decreased from 0.188 to 0.185 W/mK, while for 10 vol% BN-filled composite, it dropped from 0.778 to 0.617 W/mK.
Open Access: Yes
Biological and therapeutic implications of sex hormone-related gene clustering in testicular cancer
Publication Name: Basic and Clinical Andrology
Publication Date: 2025-12-01
Volume: 35
Issue: 1
Page Range: Unknown
Description:
Background: Gonadotropin dysregulation seems to play a potential role in the carcinogenesis of testicular germ cell tumor (TGCT). The aim of this study was to explore the expression of specific genes related to sex hormone regulation, synthesis, and metabolism in TGCT and to define specific hormonal clusters. Two publicly available databases were used for this analysis (TCGA and GSE99420). By means of hard-threshold regularized KMEANS clustering, we assigned TGCT samples into four clusters defined in respect to different expression of the sex hormone-related genes. We analysed clinical data, protein and gene expression, signaling regarding hormonal clusters. Based on whole-transcriptome gene expression, prediction of anti-cancer drug response was made by RIDGE models. Results: Cluster #1 (12–16%) consisted primarily of non-seminomatous germ cell tumor (NSGCT), characterized by high expression of PRL, GNRH1, HSD17B2 and SRD5A1. Cluster #2 (42–50%) included predominantly seminomas with high expression of SRD5A3, being highly infiltrated by T and B cells. Cluster #3 (8.3–18%) comprised of NSGCT with high expression of CGA, CYP19A1, HSD17B12, HSD17B1, SHBG. Cluster #4 (23–30%), which consisted primarily of NSGCT with a small fraction of seminomas, was outlined by increased expression of STAR, POMC, CYP11A1, CYP17A1, HSD3B2 and HSD17B3. Elevated fibroblast levels and increased extracellular matrix- and growth factor signaling-related gene signature scores were described in cluster #1 and #3. In the combined model of progression-free survival, S2/S3 tumor marker status, hormonal cluster #1 or #3 and teratoma histology, were independently associated with 25–30% increase of progression risk. Based on the increased receptor tyrosine kinase and growth factor signaling, cluster #1, #3 and #4 were predicted to be sensitive to tyrosine kinase inhibitors, FGFR inhibitors or EGFR/ERBB inhibitors. Cluster #2 and #4 were responsive to compounds interfering with DNA synthesis, cytoskeleton, cell cycle and epigenetics. Response to apoptosis modulators was predicted only for cluster #2. Conclusions: Hormonal cluster #1 or #3 is an independent prognostic factor regarding poor progression-free survival. Hormonal cluster assignment also affects the predicted drug response with cluster-dependent susceptibility to specific novel therapeutic compounds.
Open Access: Yes
Data-driven modelling of thermal conductivity in electrically aligned PDMS–diamond composites with experimental verification
Publication Name: Applied Thermal Engineering
Publication Date: 2025-12-01
Volume: 280
Issue: Unknown
Page Range: Unknown
Description:
Polymer-based composite material optimization is a key technology for achieving the desired thermal management in heat conduction sheets used in electronics and aerospace. Diamond particles are widely used as thermally conductive fillers in a liquid of poly di-methyl siloxane (PDMS) matrix because of their unique thermophysical properties. Electrical alignment is a powerful approach for filler alignment to achieve higher thermal conductivity. Meanwhile, practical experiments require substantial time, resources and consumable energy due to extensive testing. Therefore, it is essential to develop a highly robust predictive model for estimating thermal conductivity. This paper proposes a data-driven-based model that investigates a novel decision tree (DT) regression model for predicting thermal conductivity based on electrical alignment parameters, aiming to identify the optimal experimental conditions that achieve higher thermal conductivity. In this study, electrical alignment parameters, namely voltage, frequency, and rotational speed, are selected as descriptors for modelling and computing thermal conductivity. Correlation and multicollinearity analyses are conducted to evaluate the relationships among these descriptors. Three machine learning approaches, including Decision Tree, Random Forest (RF), and Gradient Boosting Decision Tree (GBDT), are investigated alongside six empirical regression models. The predictive model-based refined DT achieves high accuracy with the lowest mean square error of 0.0004 and a higher coefficient of determination (R-squared) of 0. 9751on testing data, respectively. This indicates that the model is capable of accurately predicting the thermal conductivity of hybrid nanofluids over a wide range of hybrid nanoparticle combinations with high closeness to the experimental records. This predictive model condition highlights the potential of DT-based method to precisely compute the thermal conductivity of PDMS-diamond composite based on the applied electrical alignment parameters.
Open Access: Yes
