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Applications for machine learning (ML), deep learning, and other artificial intelligence models have shown great promise in nuclear physics, including not only in classification systems but also in the analysis of numerical data. This study used various ML algorithms to estimate the concentrations of six rare earth elements (Sm, La, Ce, Sc, Eu, and Tb) in both archaeological and marine sediment samples. An interesting aspect of this analysis is that 80% of the 235 data points were used for training data, which included two parameters: specific activity ( A sp ) and concentration (ρ ) by the k0-method for the purpose of model development. The remaining 20% of the dataset was held out for testing the model's accuracy. The fundamental principle of this approach is the use of regression analysis between A sp and ρ to construct a machine learning regression model. This machine learning model was subsequently applied to estimate element concentrations based on A sp values obtained from gamma spectra. The mean absolute error (MAE), root mean square error (RMSE) and the statistical measure R-squared (R2) were employed for evaluating the accuracy of the predicted models. The random forest (RF) algorithm produces smaller MAE and RMSE values and achieves better R2 values compared to other algorithms. In addition, RF shows the lowest relative bias of the concentration values of elements in reference material (NIST 2711a) compared to other prediction models. The work focuses on demonstrating that machine learning models can effectively predict the concentrations of rare earth elements, even though this is a fundamental issue in NAA and one previous study has addressed this issue for one single element. The extension of the current work and potential directions for further development will be presented in the results and discussion section. [ABSTRACT FROM AUTHOR]

High-speed railway communication implemented based on Long-Term Evolution-Railway (LTE-R) Platform has strict requirements for quality of services to support high data-rate transmission for numerous mobile users. In such environment, there often exists inter-carrier interference. In this paper, we exploit a robust channel estimation method in the time domain for very fast time-varying LTE-railway channels, and then, we propose an effective and adaptive frequency-domain equalizer that can offer a tradeoff between performance and computational complexity. The simulation results show the robustness of our proposed method compared with previous ones. The computational complexity of the proposed method retain at a decent number by exploiting specified properties of channel matrices. [ABSTRACT FROM AUTHOR]

Seeking better surface plasmon polariton (SPP) waveguides is of critical importance to construct the frequency-agile terahertz (THz) front-end circuits. We propose and investigate here a new class of semiconductor-based slot plasmonic waveguides for subwavelength THz transport. Optimizations of the key geometrical parameters demonstrate its better guiding properties for simultaneous realization of long propagation lengths (up to several millimeters) and ultra-tight mode confinement (~λ2/530) in the THz spectral range. The feasibility of the waveguide for compact THz components is also studied to lay the foundations for its practical implementations. Importantly, the waveguide is compatible with the current complementary metal-oxide-semiconductor (CMOS) fabrication technique. We believe the proposed waveguide configuration could offer a potential for developing a CMOS plasmonic platform and can be designed into various components for future integrated THz circuits (ITCs). [ABSTRACT FROM AUTHOR]