This study presents a newly developed program that seamlessly converts G-code into formats compatible with Abaqus, enabling precise finite element simulations for FDM 3D printing. The tool operates on a two-pronged framework: a mathematical model incorporating key print parameters (layer thickness, extrusion temperature, print speed, and raster width) and a shape generator managing geometric parameters (fill density, pattern, and raster orientation). Initially, a predefined virtual section, based on predetermined dimensions, enhanced the correlation between experimental results and simulations. Subsequently, a corrected virtual section, derived from the mathematical model using the Box–Behnken methodology, improves accuracy, achieving a virtual thickness error of 1.06% and a width error of 8%. The model is validated through tensile testing of ASTM D638 specimens at 0°, 45°, and 90° orientations, using adaptive C3D4 mesh elements (0.35–0.6 mm). Results demonstrate that the corrected cross-section significantly improved simulation accuracy, reaching correlations above 95% in the elastic zone and 90% in the elastoplastic zone across all orientations. By optimizing the workflow from design to manufacturing, this program offers substantial benefits for the aerospace, medical, and automotive sectors, enhancing both the efficiency of the printing process and the reliability of simulations.

Artificial intelligence (AI) continues to advance nuclear medicine in all areas, including treatment planning, resource allocation, and precision. The imaging techniques powered by AI enable faster and more accurate diagnosis of diseases and machine learning models improve individual-specific treatment dosimetry. Additionally, AI increases operational efficiency, reduces costs, and lower radiation exposure for patients. Despite these improvements, issues such as ethical concerns, bias in data, and clinical integration difficulties still exist. This review paper discusses the role of AI in changing nuclear medicine practice, emphasizing the pros and cons, and the anticipated future. As the field proves its further value, multidisciplinary collaborations are invited to help ensure AI’s value for future nuclear medicine diagnosis and treatment.

This study presents an experimentally calibrated methodology to enhance the predictive accuracy of finite element simulations for Fused Deposition Modeling (FDM) parts in Abaqus by replacing idealized filament geometry with a physically accurate “corrected virtual raster section.” A Box-Behnken Design of Experiments (DoE) across 27 ABS specimens systematically quantifies how key printing parameters, layer thickness, raster width, extrusion temperature, and print speed, influence the true cross-sectional geometry of deposited filaments, as measured via Scanning Electron Microscopy (SEM). These data inform a predictive mathematical model that transforms the conventional circular filament shape into an experimentally grounded oval-rectangular profile, accurately capturing extrusion-induced flattening and lateral spreading. The calibrated virtual section is integrated into a custom Python-based tool that parses G-code toolpaths and sweeps the corrected geometry along deposition trajectories to generate high-fidelity, mesh-ready Abaqus models. The workflow is validated through tensile testing of ASTM D638 specimens printed at 0°, 45°, and 90° raster orientations (n=3 per orientation). Error analysis against the experimental mean demonstrates that the corrected model reduces simulation errors from catastrophic levels in the non-corrected approach (7–92% relative error, 2.5–19 MPa absolute) to engineering-grade precision (0.03–7% relative error, ≤1.3 MPa absolute). This workflow bridges G-code to physical behavior, enabling reliable simulation of FDM anisotropy.

Thallium-doped sodium iodide NaI(Tl) is one of the major scintillators that have been used for radiation detection. In this study, 2"x1.5" cylindrical NaI(Tl) detector was modelled using Monte Carlo simulation code GEANT4 in order to reproduce experimental results using standard point-like gamma sources ( ⁶⁰ Co, ²² Na, ¹³⁷ Cs) emitting mono-energetic gammas in the photon energy range 511-1332.5 keV. The modelled NaI(Tl) detector has been used to calculate the absolute and intrinsic efficiencies, as well as the mass attenuation coefficients for three different absorbers: lead (Pb), aluminum (Al), and copper (Cu) at various energies and thicknesses. The experimental and simulated data showed good agreement with a correlation coefficient (R ² ) of 0.9522 and a mean absolute error (MAE) of 2.03%, confirming the robustness of the simulation models implemented in the GEANT4 simulation toolkit and validate its use for accurately modeling the performance of NaI(Tl) detectors. The findings provide a reliable framework for extending the application of GEANT4 simulations in nuclear instrumentation, radiation protection, and medical physics.