Başkent University

This study introduces a hybrid deep learning framework that integrates convolutional neural networks (CNNs) with Kolmogorov–Arnold networks (KAN) for fine-grained classification of eight Lactarius species using a curated dataset of 1.614 images. The proposed CNN–KAN architecture significantly outperforms seven state-of-the-art baseline models, ConvNeXt-Small, EfficientNetV2-Small, MobileNetV3-Small, RegNetY-400MF, ResNet-50, SqueezeNet1.1M, and ViT-Small, across all evaluation metrics. The model achieved an accuracy of 0.9877, F1-score 0.9877, precision 0.9879, sensitivity 0.9877, specificity 0.9982, MCC 0.9855, and AUC 0.9994, representing improvements of approximately 1–3

Abstract Background The use of artificial intelligence and natural language processing technologies in healthcare services has gained significant momentum in recent years. Radiology, with its extensive textual content production and the need for report standardization, presents an ideal field of application for these technologies. This study aimed to evaluate the ability of four prominent large language models (LLMs) to accurately determine RADS scores from free-text radiology reports across five imaging modalities. Methods This retrospective cross-sectional study included 250 anonymized radiology reports obtained from a single institution between March 2024 and March 2025. Reports were drawn from thyroid ultrasound (TI-RADS), breast ultrasound and MRI (BI-RADS), prostate MRI (PI-RADS), and coronary computed tomography angiography (CAD-RADS), with 50 reports per modality. Each report was translated into English and reviewed by two radiologists to establish reference scores. The performances of ChatGPT-4o, Gemini 2.0, Claude 3.7, and Perplexity were evaluated in terms of accuracy, agreement (Cohen’s kappa), and critical misclassification rates. Results ChatGPT-4o achieved the highest overall accuracy (77.6%) and demonstrated good agreement with radiologists (κ = 0.72), followed by Claude 3.7 (64.4%, κ = 0.56), Gemini 2.0 (62.8%, κ = 0.53), and Perplexity (58.8%, κ = 0.48). Modality-specific analyses revealed the highest accuracy in CAD-RADS and BI-RADS (MRI), while the lowest performance was observed in TI-RADS. The critical misclassification rates were 6.8% for ChatGPT-4o, 10.8% for Claude 3.7, 12.0% for Gemini 2.0, and 14.0% for Perplexity. Conclusion LLMs show promising potential in supporting standardized radiology reporting, with ChatGPT-4o outperforming its counterparts across most metrics. However, limitations such as variability across modalities and non-negligible error rates highlight the need for continued refinement before clinical integration.

We obtain a three-dimensional bi-vector-tensor theory of the generalized Proca class by regularizing the Gauss-Bonnet invariant within the Weyl geometry. We show that the theory admits a regular AdS_3 black hole solution with primary hairs. Introducing a deformation in the theory, a different regular AdS_3 black hole solution is obtained. Charged generalizations of these solutions are given by coupling to Born-Infeld electrodynamics.

This study evaluates nonwoven thermoplastic fiber veils placed at the carbon fiber-aluminum interface to enhance the interlaminar fracture toughness (IFT) of carbon-reinforced aluminum laminates (CARALL). Five veils-fine glass (FG), poly(phenylene sulfide) (PPS), polyetherimide (PEI), poly(ether-ether-ketone) (PEEK), and polyimide (PI)-were interleaved in CARALL fabricated via vacuum bagging using 2024-T3 aluminum sheets and carbon/epoxy prepreg. Flexural behavior and interlaminar fracture toughness were characterized through threepoint bending, Mode-I double cantilever beam (DCB), and Mode-II end-notched flexure (ENF) tests. Thermoplastic interleaving increased flexural strength relative to the unreinforced laminate, with PEEK achieving the highest improvement (9%). In Mode-I, PPS delivered the greatest gains in crack-initiation and crack-propagation energies (157% and 69%, respectively), while FG and PI underperformed the control. Mode-II interlaminar fracture toughness (G & Iukcy;& Iukcy;c) was also maximized by PPS (128% increase). Microscopy of fracture surfaces showed that fiber bridging and fiber pull-out were common in PEEK, PEI, and PPS-interleaved laminates. This was in line with rising R-curves and longer fracture damage zones. Overall, the PPS-reinforced CARALL composite shows enhanced Mode-I/II interlaminar fracture toughness; in addition, other thermoplastic veil reinforcements demonstrate effective performance depending upon the material type.

Extreme mass-ratio inspirals (EMRIs) are among the most powerful probes of strong-field gravity and of the environments surrounding supermassive compact objects. Motivated by the expected presence of dark matter near galactic centers, we investigate the emergence and gravitational-wave imprints of chaotic dynamics in EMRIs evolving in non-vacuum spacetimes. Within a unified dynamical framework, we analyze test-particle motion in a broad class of dark-matter-embedded geometries, including singular black holes, regular black holes, naked singularities, and Einstein-cluster configurations. We show that environmental perturbations generically break integrability in the strong-field regime, giving rise to chaotic motion whose onset, duration, and termination depend sensitively on horizon structure, core regularization, and matter distribution. Using the numerical Kludge approach, we demonstrate that chaotic trajectories produce systematic qualitative modifications of the emitted gravitational radiation, such as irregular amplitude modulation and loss of phase coherence, in contrast to the smooth, quasi-periodic waveforms generated by regular motion. Our results establish the robustness of chaos in environmentally perturbed EMRIs and provide a clear conceptual link between nonlinear orbital dynamics, spacetime structure, and observable gravitational-wave signatures.