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BackgroundGaussian signals have symmetry and completeness, hence, the Gaussian filtering method is widely used in nuclear signal processing and radiation energy spectrum analysis. The mathematical description of Gaussian signals is relatively complex, which makes it difficult to construct digital Gaussian pulse shaping filters for nuclear pulse signal real-time processing. The commonly used digital quasi-Gaussian shaping algorithm in radiation measurement systems is derived from the differential equations of Sallen-Key and CR-(RC)n filters in analog nuclear electronics. However, its output shaping pulse signals have poor symmetry and problems such as undershoot occur when used in a single stage.PurposeThis study aims to explore the convolutional quasi-Gaussian pulse shaping filter algorithm and apply it to the processing of X-ray fluorescence measurement system experimental platform to obtain measured nuclear pulse data.MethodsFirstly, a convolutional quasi-Gaussian pulse shaping algorithm was implemented based on trapezoidal pulse signals, and bipolar pulse shaping was achieved after the initial convolution. Then, the second convolution was accumulated and summed to obtain left and right symmetric quasi-Gaussian shaping pulses. The digital recursive formula of the quasi-Gaussian pulse shaping algorithm was obtained using the Z-transform method, and the effectiveness of the algorithm and the influence of shaping parameters on the amplitude frequency characteristics were studied through simulation. Finally, quasi-gaussian shaping algorithm and trapezoidal shaping algorithm were applied separately to the offline process of measured nuclear pulse data from the X-ray fluorescence measurement system experimental platform.ResultsThe quasi-Gaussian pulse shaping filter has better high-frequency noise suppression performance compared to trapezoidal pulse shaping filters. With the increase of the values of shaping parameters na and nc, the filter passband decreases whilst the low-frequency amplitude relatively increases, and the high-frequency noise suppression effect is enhanced. However, this also leads to shaping pulse broadening and increases the probability of pulse pile-up.ConclusionsThe experimental results demonstrate that the quasi-Gaussian pulse shaping algorithm has better pile-up pulse separation ability. Under identical X-ray tube voltage and current conditions and peaking time, the energy resolution of the energy spectra obtained by both algorithms is fundamentally equivalent. However, the energy spectrum with quasi-Gaussian pulse shaping has a higher characteristic peak area.

BackgroundIn cave disposal environment, surrounding rock is the last barrier to prevent radionuclides from entering the environment, and the colloid of surrounding rock produced in the long-term disposal process increases the risk of radionuclides migration.PurposeThis study aims to explore the adsorption performance of surrounding rock colloid for Sr2+ disposed the repository, and the stability of surrounding rock colloid.MethodsFirst of all, the surrounding rock colloid sample was prepared with approximate mass concentration of 0.03 g·L-1, zeta potential of 21.53 mV and particle size of 205.7 nm. Then, the effects of time, pH value, various ions under different concentrations, and other factors on the adsorption properties, such as the zeta potential and average particle size of the surrounding rock colloids were investigated by experimental measurements. Finally, the adsorption kinetics and adsorption isothermal model were analyzed.ResultsExperimental results show that the colloid adsorption Sr2+ reaches equilibrium at 12 h, and the equilibrium adsorption capacity is 41.79 mg·g-1. In alkaline environment, the adsorption capacity increases with the increase of pH. The adsorption of Sr2+ by colloid of surrounding rock is inhibited by different ions, and the inhibitory effect of cations is greater than that of anions.ConclusionsResults of this study indicate that pH value, temperature and partial ions in the groundwater of the cave disposal facility all have influence on the stability of surrounding rock, and the surrounding rock colloid has good stability in the groundwater environment of the repository. The adsorption of Sr2+ by surrounding rock colloid conforms to the quasi-second-order kinetic model and the Freundlich adsorption isothermal model.

BackgroundNeutrons/Gamma (n/γ) discrimination is critical for neutron detection in the presence of γ radiation and traditional pulse shape discrimination methods suffer from unstable discrimination accuracy.PurposeThis study aims to implement a machine-learning method that combines the kernel principal component analysis (KPCA), marine predator algorithm (MPA), and extreme learning machine (ELM) is proposed to improve the n/γ discrimination efficiency and accuracy against the traditional pulse shape discrimination methods.MethodsThe KPCA was used to reduce the dimensionality of the pulse signal characteristics of neutrons and gamma rays. Owing to the randomness in the ELM input layer weight and hidden layer bias, the MPA was employed to optimize the foregoing factors to improve the n/γ discrimination accuracy of the ELM. Finally, experimental data of Pu-C neutron source using BC-501A liquid scintillator detector were applied to effectiveness comparison of training and test with and without KPCA dimensionality reduction.ResultsComparison results reveal that the average discrimination accuracy of the KPCA-MPA-ELM is as high as 99.07%, which is 12.19%, 2.52%, and 1.56% higher than those of the ELM, MPA-ELM, and KPCA-ELM models, respectively. Compared with the charge comparison method and pulse gradient analysis method, the accuracy is improved by 1.80% and 5.91%, respectively.ConclusionsThe proposed model has a simple structure, exhibits good stability, hence be applied to handling high-dimensional data with good discrimination and generalization ability.

During the neutron detection process, owing to the effects of inelastic scattering and slow neutron capture, a neutron-gamma mixed radiation field is formed, which increases the complexity of neutron detection. Organic scintillators are widely used in neutron detection because of their high flashing efficiency, short decay time, and high detection efficiency. Pulse shape discrimination (PSD) is a key technology for discriminating neutrons and gamma rays according to the difference in pulse shape caused by the difference in particle decay time in organic scintillators. Traditional PSD methods include time-domain and frequency-domain discrimination methods. In recent years, various machine-learning techniques applied to neutron-gamma discrimination have achieved better results. To better use organic scintillators and the corresponding neutron-gamma discrimination methods in neutron detection, we conducted a comprehensive analysis of the glowing mechanism of organic scintillators, PSD principle, organic scintillator types, and neutron-gamma discrimination methods and investigated the performance evaluation indexes of organic scintillators and neutron-gamma discrimination methods. Finally, the future development directions of organic scintillators and neutron-gamma discrimination methods were examined.

BackgroundAt present, radioactive waste is treated by deep geological treatment. Caves-type repository as a geological repository near the ground, are more and more international and domestic concern due to its nuclear safety, low environmental pollution and good economy. A cavern-type low and intermediate radioactive waste repository is being constructed in Yangjiang area of Guangdong province, China, the surrounding rock of the waste repository will be the main dispose material of nuclide 137Cs.PurposeThis study aims to investigate the adsorption performance of the surrounding rock of this cavern-type low and intermediate radioactive waste repository for 137Cs.MethodsFirst of all, series of characterization analysis, such as X-ray Fluorescence (XRF), Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM), Brunauer-Emmett-Teller (BET), Barret-Joyner-Halenda (BJH) were employed to observe crystal structure and content of the surrounding rock of the waste repository. Irregular sheet-like structures stacked, the sheets in a compacted state, and the surface groups such as -OH, Si-O-Si, Si-O-Al, Si-O, etc. were shown to have good crystal structure and the highest SiO2 composite content. Then, the effects of time, pH value, solid-liquid ratio, initial radioactive activity concentration, and ions on the adsorption were investigated through static adsorption experiments. Finally, the adsorption kinetics and adsorption isotherm models were analyzed.ResultsThe results show that when the solid-liquid ratio is 5 g∙L-1, the initial radioactivity is 1 114.5 Bq∙L-1, the adsorption equilibrium time is 12 h, the equilibrium adsorption rate is 40.41%, and the equilibrium adsorption capacity is 90.07 Bq∙g-1. The adsorption rate increases with the increase of the solid-liquid ratio, and decreases with the increase of the initial radioactivity. The adsorption is inhibited under acidic conditions, but has little effect under neutral and alkaline conditions. Different ions inhibit the adsorption of 137Cs on the surrounding rock of the repository, and K+ has the greatest inhibition.ConclusionsThe adsorption of 137Cs by the surrounding rock is more in line with the pseudo-second-order kinetic model and the Freundlich adsorption isotherm model, providing the basic data for nuclear waste geological disposal engineering.

BackgroundCement mortar is an essential building and barrier material for geological disposal repositories of radioactive waste. Therefore, it is of great significance to study the preparation of the cement mortar and its adsorption property and mechanism for radionuclides.PurposeThis study aims to study the preparation of M15 cement mortar and its adsorption property as well as mechanism for Co2+.MethodsA cavern-type low and/or intermediate-level radioactive waste repository under construction in the South China was selected as a real reference for the design and manufacture of M15 cement mortar. Adsorption properties of the M15 cement mortar for Co2+ were determined through the static batch test method combined with the inductively coupled plasma atomic emission spectrometry analysis. Characterization analyses of the M15 cement mortar were performed by X-ray fluorescence spectrometer, X-ray diffractometer, infrared spectrometer and scanning electron microscope.ResultsCompressive strength tests show that the M15 cement mortar with cement:sand:water mass ratio of 1.0∶4.84∶0.84 meets compressive requirements. The characterization analyses reveal that the M15 cement mortar had good crystal structure, large specific surface area, amorphous distribution of particles and surface distributed with a large number of groups, such as OH-, HCO3-, CO32-, SiO42-. Adsorption results from the static batch test show that: (1) the adsorption equilibrium time, adsorption distribution coefficient (Kd) value, adsorption rate and adsorption capacity of the M15 cement mortar are 6 h, 1 453.93 mL∙g-1, 74.14% and 73.37 mg∙g-1, respectively, (2) the Kd value, adsorption rate and adsorption capacity increase with the decrease of particle size, (3) the Kd value is 1 626.02 mL∙g-1 when the solid-liquid ratio is 2 g∙L-1, indicating that the M15 cement mortar has good absorption capability for Co2+, (4) the Kd values rise with the increase of pH values of water phase, and (5) the order of inhibition effects of ions on the Co2+ adsorption is Mg2+>Ca2+>SO42->NO3->Cl->K+. Adsorption mechanism of the M15 cement mortar for Co2+ can be described with the pseudo-second-order kinetic model and Langmuir adsorption isotherm model.ConclusionsThis study provides a useful reference for the preparation of superior performance cement mortar to build disposal repositories of radioactive waste.

Foam mixers are classified as low-pressure and high-pressure types. Low-pressure mixers rely on agitator rotation, facing cleaning challenges and complex designs. High-pressure mixers are simple and require no cleaning but struggle with uneven mixing for high-viscosity substances. Traditionally, increasing the working pressure resolved this, but material quality limits it at higher pressures. To address the issues faced by high-pressure mixers when handling high-viscosity materials and to further improve the mixing performance of the mixer, this study focuses on a polyimide high-pressure mixer, identifying four design variables: impingement angle, inlet and outlet diameters, and impingement pressure. Using a Full Factorial Design of Experiments (DOE), the study investigates the impacts of these variables on mixing unevenness. Sample points were generated using Optimal Latin Hypercube Sampling—OLH. Combining the Sparrow Search Algorithm (SSA), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM), the SSA-CNN-LSTM model was constructed for predictive analysis. The Whale Optimization Algorithm (WOA) optimized the model, to find an optimal design variable combination. The Computational Fluid Dynamics (CFD) simulation results indicate a 70% reduction in mixing unevenness through algorithmic optimization, significantly improving the mixer's performance. [ABSTRACT FROM AUTHOR]

Liquor has a complex system with high dimensional components. The trace components in liquor are varied and have low content and complex coordination relationships. This study aimed to solve the problem of reliance on smell and taste. Based on the characteristics of near-infrared spectrum response to hydrogen-containing groups, qualitative analysis was carried out in combination with machine learning technology. Firstly, an iterative adaptive weighted penalized least squares algorithm with spectral peak discrimination was used for baseline correction to effectively retain useful information in the feature absorption peaks. Then, the convolution smoothing algorithm was used to filter the noise, and the spectral curve smoothness was adjusted using the convolution window width. The near-infrared spectrum has a high dimension. Monte Carlo random sampling combined with an improved competitive adaptive reweighting method was used to evaluate the importance of spectral sampling points. According to the importance coefficient, the dimension of the spectral data set was optimized by using an exponential attenuation function through an iterative operation, and the data set with the smallest root-mean-square error was taken as the characteristic spectrum. The nonlinear separability of characteristic spectra was further improved by kernel principal component analysis. Finally, a liquor quality recognition model based on principal component analysis was established by using the hierarchical multiclass support vector machine method. Our key findings revealed that the prediction accuracy of the model reached 96.87% when the number of principal components was 5–12, with more than 95% of the characteristic information retained. These results demonstrated that this rapid nondestructive testing method resolved the challenge posed by relying on subjective sensory evaluation for liquor analysis. The findings provide a reliable analytical approach for studying substances with high-dimensional component characteristics. [ABSTRACT FROM AUTHOR]

3He is a splendid neutron detection material due to its high neutron reaction cross section, gaseous state, and nonelectronegative and nonpoisonous nature. With the worldwide problem of the "3He supply crisis" arising, boron-lined gaseous neutron detectors are being widely used in neutron detection to replace 3He neutron detectors. In this work, to reduce the scattering neutron background coming from the substrate of a boron-lined neutron detector in the application of neutron scattering, a new design of the boron-lined gaseous neutron detector composed of a boron-lined multichip converter and a multiwire proportional chamber was proposed. The electron drift efficiency matrix simulated by Garfield++ (Version 2023.4) and the values and positions of electron energy deposition simulated by Geant4 were obtained. The α, 7Li, and total charged particle energy deposition spectra were acquired via coupling calculations of the electron drift efficiency matrix and the values and positions of electron energy deposition, and the width of the slit was selected as 3 mm. The boron-lined multilayer converter neutron detector (BMCND) was tested using a 241Am–239Pu mixture α source, and the total count rate of α charged particles was measured as 599.5 s−1, which is 89% of the theoretical α particle emission rate of 672.9 s−1. The drift voltage experiments showed that 1200 V is enough to acquire a relatively ideal count, and a 2500 V drift voltage was confirmed, considering the higher count and instrument safety. We also performed the neutron detection experiments using a photo-neutron source, and a characteristic spectrum shape of "two stairs" was measured. When borated polyethylene was used to shield the BMCND, the detected total count decreased while keeping the characteristic spectrum shape, demonstrating that the BMCND was equipped with the ability to detect neurons, indicating that BMCNDs have the potential to be an outstanding 3He alternative neutron detector. [ABSTRACT FROM AUTHOR]

Detecting transportation pipeline leakage points within chemical plants is difficult due to complex pathways, multi-dimensional survey points, and highly dynamic scenarios. However, hexapod robots' maneuverability and adaptability make it an ideal candidate for conducting surveys across different planes. The path-planning problem of hexapod robots in multi-dimensional environments is a significant challenge, especially when identifying suitable transition points and planning shorter paths to reach survey points while traversing multi-level environments. This study proposes a Particle Swarm Optimization (PSO)-guided Double Deep Q-Network (DDQN) approach, namely, the PSO-guided DDQN (PG-DDQN) algorithm, for solving this problem. The proposed algorithm incorporates the PSO algorithm to supplant the traditional random selection strategy, and the data obtained from this guided approach are subsequently employed to train the DDQN neural network. The multi-dimensional random environment is abstracted into localized maps comprising current and next level planes. Comparative experiments were performed with PG-DDQN, standard DQN, and standard DDQN to evaluate the algorithm's performance by using multiple randomly generated localized maps. After testing each iteration, each algorithm obtained the total reward values and completion times. The results demonstrate that PG-DDQN exhibited faster convergence under an equivalent iteration count. Compared with standard DQN and standard DDQN, reductions in path-planning time of at least 33.94% and 42.60%, respectively, were observed, significantly improving the robot's mobility. Finally, the PG-DDQN algorithm was integrated with sensors onto a hexapod robot, and validation was performed through Gazebo simulations and Experiment. The results show that controlling hexapod robots by applying PG-DDQN provides valuable insights for path planning to reach transportation pipeline leakage points within chemical plants. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments. [ABSTRACT FROM AUTHOR]

The velocity model is one of the main factors affecting the accuracy of microseismic event localization. This paper addresses the issue of the low accuracy of microseismic event localization in tunnels and, combined with active-source technology, proposes a "source–station" velocity model. The velocity model assumes that the velocity from the source to each station is different, and it can greatly improve the accuracy of the time-difference-of-arrival algorithm. At the same time, for the case of multiple active sources, the MLKNN algorithm was selected as the velocity model selection method through comparative testing. The results of numerical simulation and laboratory tests in the tunnel showed that the average location accuracy of the "source–station" velocity model was improved compared with that of the isotropic velocity and sectional velocity models, with numerical simulation experiments improving accuracy by 79.82% and 57.05% (from 13.28 m and 6.24 m to 2.68 m), and laboratory tests in the tunnel improving accuracy by 89.26% and 76.33% (from 6.61 m and 3.00 m to 0.71 m). The results of the experiments showed that the method proposed in this paper can effectively improve the location accuracy of microseismic events in tunnels. [ABSTRACT FROM AUTHOR]