A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting.

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials' shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam γ-ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to 1006 kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete's γ-ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and R2score were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE.

Simulation and prediction of the attenuation behaviour of the KNN-LMN-based lead-free ceramics by FLUKA code and artificial neural network (ANN)-based algorithm.

The significance and novelty of the present work are the preparation of the non-lead ceramic by the general formula of (1-x) K0.5Na0.5NbO3-xLa Mn0.5Ni0.5O3 (KNN-LMN) with different x (0(HVL)x=0.04>(HVL)x=0.07>…>(HVL)x=0.25 is reported for half-value layer values against gamma photon. From the attained results, it can be concluded that increaisng the rate of x results in the better shielding proficiency in terms of neutron and gamma photon for chosen KNN-LMN-based lead-free ceramics.

An extensive study on the neutron-gamma shielding and mass stopping power of (70-x) CRT-30K2O-xBaO glass system for 252Cf neutron source.

The present study focuses on the charged-uncharged particles shielding performance of the addition of a mixed type of cathode ray tube (CRT) in a glass system that is irradiated by the 252Cf neutron source via the MCNPX simulation and analytical calculations, as well as Phy-X: PSD and SRIM software. The CRT waste glass is inserted into the glass system with (70-x) CRT-30K2O-xBaO general formula for x = 0, 10, 20 mol% that produces CG1, CG2, and CG3 glass shielding materials. Using Watt Fission Distribution (WFD) and Doppler Effect (DE) the neutron-gamma photon spectra were extracted for shielded (in the presence of the glass materials) and unshielded (in air) cases. Some calculated attenuation parameters related to the neutron deduced that CG1 is the best neutron attenuator among the selected glass samples. Moreover, by increasing the density of the glass from CG1 to CG3, the ascending trend is observed for the linear attenuation coefficient (LAC, cm-1) of the studied glass, and the best shielding competence is monitored for CG3. Furthermore, two sharp peaks are found in Zeff graphs which may be due to K-edge absorption of Ba and Pb elements and by decreasing the Pb element from CG1 to CG3 the second peak gradually becomes smooth. In addition, Mass Stopping Power/ Projected Ranges of the proton (H1) and alpha particles (He+2) are also estimated by SRIM code and findings show that CG1 can better stop proton and alpha particles in comparison with the other chosen glass structures.