The microscopic pore structure is of great significance to the study of concrete materials. However，the traditional pore structure testing methods， such as nuclear magnetic resonance (NMR)，mercury intrusion porosimetry (MIP)，optical microscopy，X-ray tomography (X-CT)，and nitrogen adsorption，still have some limitations in terms of pore morphology and nanoscale pore characterization. In this study，we proposed to use argon ion polishing-scanning electron microscopy method to study the microscopic pore structure in concrete，prepared concrete samples with high-quality mechanically damage-free surfaces， and qualitatively investigated four types of pore structure in concrete. Large-area backscattered electron images were collected, and the pore porosity，pore diameter，pore shape factor，and graded porosity of the pore structure were quantified by ImageJ，Avizo，and other software. This method achieves the qualitative analysis and quantitative characterization of concrete microporous structure, which is of reference significance for the research and analysis of the microstructure of concrete materials.

The rapid identification of radionuclides is a critical component of nuclear material detection systems，essential for improving the performance and efficiency of radiation detection. Traditional nuclide spectrum recognition methods typically involve multiple complex steps，such as noise reduction，background subtraction，and feature extraction，which are computationally intensive，time-consuming，and inefficient，making them unsuitable for rapid response in practical applications. To address these issues，this paper proposed a rapid nuclide spectrum recognition algorithm based on the MobileNetV3 neural network，which achieved efficient nuclide recognition by optimizing data processing and model training methods. A series of simulated datasets were generated using Monte Carlo (MCNP) simulation software，including scenarios with different radioactive sources and particle counts，varying distances between NaI detectors and the sources，and mixed nuclide environments. These diverse datasets were used to train and validate the network model，enhancing its generalization capability. To better process the full-energy peak characteristics of gamma spectra，this study designs a preprocessing method based on a sliding window approach，which incrementally transforms one-dimensional spectral data. Subsequently，the transformed spectral data is mapped into two-dimensional grayscale images using Hilbert curves and input into the MobileNetV3 model for training and prediction. Experimental results demonstrate that the proposed neural network model performs exceptionally well in rapidly processing spectrum data handled by the sliding window method，achieving high-precision recognition of different nuclides while maintaining efficient learning. In terms of model performance，using sliding window sizes of 23 and 25 results in faster convergence and significantly improved recognition accuracy. This study highlights the effectiveness of integrating deep learning with nuclide spectral characteristics，providing a novel and efficient solution for nuclear material detection systems.

In order to accurately measure ²³²Th in the waste residues of associated radioactive mines, especially for the measurement of slag samples under non-equilibrium conditions, and to provide data support for radiation environment supervision, a method for measuring ²³²Th in slag using a high-purity germanium γ spectrometer was established. The method involves indirect measurement by selecting the characteristic γ rays of the daughter nuclides in the decay chain of ²³²Th, and calculating its specific activity by the relative comparison method. The slag samples in a non-equilibrium state are sealed for 20 days to enable ²²⁸Ra and ²²⁸Ac to reach equilibrium, and ²²⁸Th, ²²⁴Ra and subsequent daughter nuclides to reach equilibrium. Then, the specific activity of the parent nuclide ²³²Th is obtained through conversion relationships. This method is accurate and efficient, with a detection limit of 1.1 Bq・kg^-1, a precision better than 5 %, and a relative error lower than 5 %. The method of measuring ²³²Th in slag using a high-purity germanium γ spectrometer does not require complex chemical treatment, which makes up for the deficiencies of inductively coupled plasma mass spectrometry. The calculation method under non-equilibrium conditions is also more reliable than using the average value of a single characteristic peak or multiple characteristic peaks.