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Neutron activation analysis is a highly sensitive non-destructive testing technique with important applications in industry, geoscience, medical therapy, etc. This work designed and optimized a thermal neutron device that utilized a portable D-D neutron generator, and the Monte Carlo method with the Geant4 toolkit was applied to simulation. The objective of the optimized design is to maximize the thermal neutron flux at the output surface and increase the utilization efficiency of the neutron generator. A parameter K was defined as a measure of the device's slowing capacity for neutrons and was used to determine the optimized device geometry. The simulation considered the contribution of different types and sizes of moderators and reflectors to the thermal neutron intensity to obtain the optimal size. The shielding protection of the device was then designed. The effectiveness of shielding with different thicknesses was evaluated using three dose reference points. The results indicated that the optimized device can achieve a maximum thermal neutron flux of 1.97 × 10 5 n∙cm -2 ∙s -1 at the output surface by using high-density polyethylene (HDPE) as the moderator and nickel as the reflector. It was determined that using 45 cm of HDPE and 9 cm of lead protection in sequence along the neutron head axis would reduce the dose rate at the reference point, located 5 cm from the surface of the device, below the safety limit of 2.5 μSv/h., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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.

Achieving efficient uranium adsorption from highly acidic wastewater is still considered challenging. Here, an inorganic-organic hybridized self-assembly material (rPFE-10) with redox activity was constructed by phytic acid (PA), ethylenediamine (EDA), and Fe(II) via a facile one-pot route, and further applied for U(VI) removal. In the static adsorption experiment, rPFE-10 achieved the maximum U(VI) adsorption capacity of 717.1 mg/g at the optimal pH of 3.5. It also performed preeminently in a highly acidic condition of pH = 1.0, with the highest adsorption capacity of 551.2 mg/g and an equilibrium time of 30 min. Moreover, rPFE-10 exhibited a pH-responsive adsorption selectivity for U(VI) and An-Ln (S (U(VI) ) and S (An-Ln) ), which increased to 69 % and 94 % respectively as pH decreased from 3.0 to 1.0. Additionally, the spectral analysis revealed a reconstruction mechanism induced by multiple synergistic adsorption, in which U(VI) exchange with EDA +/2+ and Fe 2+/3+ and earned suitable coordination geometry and ligand environment to coordinate with PA (mainly P-OH), while partial U(VI) is reduced by Fe(II) in framework. This work not only highlights the facile strategy for enhanced U(VI) retention in highly acidic solution, but expands the potential application of supramolecular self-assembly material in treatment of nuclear wastewater., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Segmented gamma scanning (SGS) is a fast and effective method for measuring radioactive waste drum. The efficiency calibration is directly related to the accuracy of reconstructed radioactivity. An efficiency function model and SGS efficiency calibration method are proposed for solving existing SGS efficiency calibration problems such as time lag, limited by experimental sources or difficult to effectively combine with SGS system. The SGS system model is established by the Geant4 to calculate the segment efficiency under different linear attenuation coefficients of medium and gamma energies. The efficiency calibration function is established with the function model and parameters. Waste drum samples are constructed with the polyethylene and point sources 137 Cs/ 60 Co to complete SGS experimental measurement, efficiency calibration and radioactivity reconstruction. The result shows that the relative deviation of the reconstructed activity of a single point source at different locations in the drum is -50.48% to 43.69% and it of multi-point sources in a segment or a drum is -27.88% to 3.57%. Experimental results verify the effectiveness of this efficiency function model and SGS calibration method., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Honglong Zheng reports financial support was provided by Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things. Honglong Zheng reports financial support was provided by National Natural Science Foundation Committee of China. Qi Liu reports financial support was provided by Sichuan Provincial Department of Science and Technology of China. Yuhao Li reports financial support was provided by University Student Innovation and Entrepreneurship Training Program of China., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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.

The detection speed of the tomographic gamma scanning (TGS) system with a detector array is faster than the single detector system. The NaI(Tl) detector is inexpensive and can work at room temperature, making it ideal for use in the TGS system with a fan-shaped detector array. The collimator of the TGS system is one of the critical elements to ensure the reconstructed image's quality. In addition to providing good detection efficiency of the detector while improving the system's spatial resolution, a proper collimator structure may also reduce cross-interference between segments. We propose a collimator design method for the TGS system with a fan-shaped NaI(Tl) detector array and combine it with the Monte Carlo method to optimize the collimator. We get the collimator aperture size and shape of the TGS system through the simulation results. Simultaneously, according to the detectors' equiangular sector arrangement limitation, we propose setting up a fan-shaped shield with adjustable depth and height at the detector collimator's front. The cross-interference between segments is effectively reduced without reducing the current segment's detection efficiency. The transmission image reconstruction shows that the collimator designed by this method can be used in the TGS system with the fan-shaped NaI(Tl) detector array., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The Alxa region (Inner Mongolia, China) is one of the areas preselected for use as a geological repository of high-level radioactive waste in China. Radioactive waste produces radioactive rays during long-term storage, and the cumulative absorbed dose in 1000 years can significantly exceed the maximum of 0.7 MGy, thereby challenging the long-term adsorption stability of clay. This study employed 60 Co gamma (γ)-rays to irradiate clay in air under a dose rate of 10 kGy/h. The changes in the internal structure and mechanisms of clay under different gamma radiation doses (1, 2, and 3 MGy) were investigated. Additionally, the adsorption properties of irradiated clay for U(Ⅵ) were tested under different conditions. The clay samples underwent minimal structural changes following high-dose irradiation, and the interlayer spacing was altered due to the fractured framework, dehydroxylation, and radiolysis of water. After irradiation, the Fe (Ⅱ) content in clay was significantly increased, unlike Fe (Ⅲ) content. The adsorption mechanisms of clay before and after the experiments were verified, revealing that the adsorption capacity of irradiated clay to U(Ⅵ) is reduced., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

One-way-broadcast-based flooding time synchronization algorithms are commonly used in wireless-sensor networks (WSNs). However, the packet delay and clock drift pose a challenge to accuracy, as they entail serious by-hop error accumulation problems in the WSNs. To overcome this, a rapid-flooding multibroadcast time synchronization with real-time delay compensation (RDC-RMTS) is proposed in this article. By using a rapid-flooding protocol, flooding latency of the referenced time information is significantly reduced in the RDC-RMTS. In addition, a new joint clock skew-offset maximum-likelihood estimation (MLE) is developed to obtain the accurate clock parameter estimations and the real-time packet delay estimation. Moreover, an innovative implementation of the RDC-RMTS is designed with an adaptive clock offset estimation. The experimental results indicate that the RDC-RMTS can easily reduce the variable delay and significantly slow the growth of by-hop error accumulation. Thus, the proposed RDC-RMTS can achieve accurate time synchronization in large-scale complex WSNs.

The finite-time attitude cooperative control problem for a group of multiple unmanned aerial vehicle systems with external disturbances and uncertain parameters is discussed in this paper. The dynamics of the systems is described by quaternion avoiding the singularity. Based on the attitude error and angular velocity error, a novel nonsingular terminal sliding mode surface is proposed for the controller with event-triggered scheme. The lumped disturbances are estimated by neural networks with adaptive law. The communication frequency is decreased by the proposed distributed event-triggered based sliding mode controller. Lyapunov theory is utilized to analyze the stability of the systems, and the Zeno behavior is avoided by rigorous proof. Finally, simulation examples are presented to illustrate the efficiency of the proposed control algorithm., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Qiang Han et al.)

Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors, and its results are inaccurate and unstable. This study developed a near-infrared (NIR) spectral characteristic extraction method based on a three-dimensional analysis space and establishes a high-accuracy qualitative identification model. First, the Norris derivative filtering algorithm was used in the pre-processing of the NIR spectrum to obtain a smooth main absorption peak. Then, the third-order tensor robust principal component analysis (TRPCA) algorithm was used for characteristic extraction, which effectively reduced the dimensionality of the raw NIR spectral data. Finally, on this basis, a qualitative identification model based on support vector machines (SVM) was constructed, and the classification accuracy reached 98.94%. Therefore, it is possible to develop a non-destructive, rapid qualitative detection system based on NIR spectroscopy to mine the subtle differences between classes and to use low-dimensional characteristic wavebands to detect the quality of complex multi-component mixtures. This method can be a key component of automatic quality control in the production of multi-component products.

Coded-aperture imagers typically have a smaller field-of-view (FOV) than in un-collimated gamma imaging systems. However, sources out of the fully coded field-of-view (FCFOV) can cause pseudo hotspots on the wrong side of an image reconstructed using the cross-correlation method. In this work, we propose a neural network method to identify and localize the sources within the partially coded field-of-view (PCFOV). The model was trained using Monte Carlo simulation data and evaluated with both simulation and experimental data. The results showed that the proposed model can identify and localize sources with good classification accuracy, low positioning error, and strong robustness to the statistical noise., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

This paper proposes a new method to reject pile-up pulse continuous zone. This paper first simulates the differences between three kinds trapezoidal shaping methods of which are trapezoidal shaping without width discrimination, with fixed width discrimination or with changing width discrimination, and then carry out experiment with silicon drift detector (SDD) and 55 Fe source in laboratory, and the result of the experiment is that the ratio of peak to background (P/B) increases while the characteristic peaks in high energy section are not affected. Result of the simulation and experiment shows that the method proposed in this paper makes progress in pile-up pulse continuous zone rejection under the circumstance of high-count rates., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Aimed at locating the temperature abnormal event of nuclear waste drums in a nuclear waste temporary storage repository by a Raman-based distributed temperature sensor, a principal component analysis (PCA)-based method for application is proposed. The effectiveness of the proposed method is verified in the physical simulation device of the nuclear waste drums. First, some samples of the temperature abnormal event with known location are taken as the reference samples, and their features are extracted by PCA. Then, the features of the test sample data to be located are also extracted by PCA. The Euclidean distance is used to measure the similarity between the features of the test sample and the feature of each reference sample. Finally, we find the reference sample that is most similar to a test sample, the location of which is considered the location of the temperature anomaly event for the test sample. The experimental results show that the proposed method can accurately locate the temperature abnormal event of the nuclear waste drums, and the accuracy rate can reach 96%. The method that is proposed in this paper can reliably locate the temperature abnormal event generated by the nuclear waste temporary storage repository induced by external factors such as landslides or earthquakes, and provide technical support for nuclear safety.

Hypothesis: Nanoparticles functionalized with ligands which can on-demand and remotely be detached have recently attracted interest as stimuli-responsive materials. Research is now focused on multi-responsive systems, with applications in environmental science and biomedicine. The possibility to covalently couple two different ligands on a single nanoparticles, and to release them independently is investigated. This concept of nanoparticles functionalized with dual on-demand cleavable ligands is exploited in ground water decontamination and radionuclides separation. Efficient separation of contaminants in a single step is expected, simplifying partitioning process and decreasing generation of secondary waste by nuclear industry., Experiments: Sub-10 nm Janus superparamagnetic nanoparticles are functionalized by click-chemistry (thiol and Diels-Alder) with two different Actinide-specific chelators. The reversible covalent bonds allow to detach chelators independently by either pH- or thermo-stimulation. The nanoparticles decorated with diethylenetriamine-pentaacetic acid (DTPA) and [(2-furan-2-yl-2-hydroxy-ethylcarbamoyl)-methoxy]-acetic acid (FHECMAA) are incubated with UO 2 2+ and La 3+ (as substitute for Pu 3+ ) at pH = 3 and 7 before chelator-metal complexes are released. Metal contents are measured to determine separation efficiency., Findings: Chelators can be detached from Janus nanoparticles with perfect selectivity. The nanoparticles are highly efficient for extraction of metals in acidic medium and show good ability for separation of U and La at neutral pH., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The ability of bentonite and montmorillonite pillared by Al-oxide and mixed (Ln-Al)-oxides (Ln = La, Ce) to remove 239 plutonium solution species from water is comparatively investigated at pH 7 and pH 4. Small-angle scattering and neutron contrast variation with H 2 O/D 2 O mixtures is used to verify the ingress of water in the calcined products after hydrophilicity was introduced by an NH 3 -H 2 O vapor treatment. The size and shape of the (La/Ce)-Al oxo-hydroxy pillaring cations (2 nm spheres) is determined by small-angle x-ray scattering from the pillaring solutions. Not all of the oxide pillars improved Pu uptake compared with sodium montmorillonite. At neutral and acidic pH only (Ce-Al)-oxide pillared clays showed the ability to remove Pu over the concentration range studied (1.35 × 10 -8 -8 × 10 -8  mol dm -3 ) with distribution coefficient (K D ) values >10 4 . XPS analysis of the (Ce-Al)-oxide pillared clays indicates the presence of Ce 4+ as cerium dioxide. The progressive improvement in sorption performance in the order of pillar type Al 2 O 3  < La 2 O 3 -Al 2 O 3  << CeO 2 -Al 2 O 3 reflects the increasing access of Pu solution species to the clay mineral layers by changes to the basal spacing and specific surface area, and also to the higher stability of the (Ce-Al)-oxide pillars., (Copyright © 2018 Elsevier B.V. All rights reserved.)

To improve the visible light absorption and photocatalytic activity of titanium dioxide nanotube arrays (TONTAs), ZnFe 2 O 4 (ZFO) nanocrystals were perfused into pristine TONTA pipelines using a novel bias voltage-assisted perfusion method. ZFO nanocrystals were well anchored on the inner walls of the pristine TONTAs when the ZFO suspensions (0.025 mg mL -1 ) were kept under a 60 V bias voltage for 1 h. After annealing at 750 °C for 2 h, the heterostructured ZFO/Fe 2 TiO 5 (FTO)/TiO 2 composite nanotube arrays were successfully obtained. Furthermore, Fe 3+ was reduced to Fe 2+ when solid solution reactions occurred at the interface of ZFO and the pristine TONTAs. Introducing ZFO significantly enhanced the visible light absorption of the ZFO/FTO/TONTAs relative to that of the annealed TONTAs. The coexistence of type I and staggered type II band alignment in the ZFO/FTO/TONTAs facilitated the separation of photogenerated electrons and holes, thereby improving the efficiency of the ZFO/FTO/TONTAs for photocatalytic degradation of methylene blue when irradiated with simulated sunlight.

The fluence rate of cosmic-ray-induced neutrons (CRINs) varies with many environmental factors. While many current simulation and experimental studies have focused mainly on the altitude variation, the specific rule that the CRINs vary with geomagnetic cutoff rigidity (which is related to latitude and longitude) was not well considered. In this article, a double-exponential fitting function F=(A1e-A2CR+A3)eB1Al, is proposed to evaluate the CRINs' fluence rate varying with geomagnetic cutoff rigidity and altitude. The fitting R2 can have a value up to 0.9954, and, moreover, the CRINs' fluence rate in an arbitrary location (latitude, longitude and altitude) can be easily evaluated by the proposed function. The field measurements of the CRINs' fluence rate and H*(10) rate in Mt. Emei and Mt. Bowa were carried out using a FHT-762 and LB 6411 neutron prober, respectively, and the evaluation results show that the fitting function agrees well with the measurement results., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

An algorithm based on moment estimation is presented to determine the initial parameters of the particle spectrum peak shape function for the iteration fitting procedure. The algorithm calculates the mean value, variance, and third-order central moment by using the spectrum peak data, solves the parameters of the fitting function, and then provides them as the initial values to the Levenberg-Marquardt algorithm to ensure convergence and optimized fitting. The effectiveness of the proposed algorithm was tested by gamma and alpha spectra. The algorithm can be used in automated peak curve fitting and spectral analysis., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data., Competing Interests: The authors declare no conflict of interest.

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.

This study proposes a novel natural environment background model by modeling brief environment conditions. It uses Geant4 program to simulate decays of (238)U, (232)Th, and (40)K in soil and obtains compositions of different-energy gamma rays in the natural environment background. The simulated gamma spectrum of the natural environment background agrees well with the experimental spectrum, particularly above 250 keV. The model is used in the simulation of anticoincidence measurement, indicating that the natural environment background can be decreased by approximately 88%, and the Compton attenuation factor is 2.22. The simulation of anticoincidence measurement can improve the minimum detectable activity (MDA) of the detection system., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Clustering analysis is a promising data-driven method for analyzing functional magnetic resonance imaging (fMRI) time series data. The huge computational load, however, creates practical difficulties for this technique. We present a novel approach, integrating principal component analysis (PCA) and supervised affinity propagation clustering (SAPC). In this method, fMRI data are initially processed by PCA to obtain a preliminary image of brain activation. SAPC is then used to detect different brain functional activation patterns. We used a supervised Silhouette index to optimize clustering quality and automatically search for the optimal parameter p in SAPC, so that the basic affinity propagation clustering is improved by applying SAPC. Four simulation studies and tests with three in vivo fMRI datasets containing data from both block-design and event-related experiments revealed that functional brain activation was effectively detected and different response patterns were distinguished using our integrated method. In addition, the improved SAPC method was superior to the k -centers clustering and hierarchical clustering methods in both block-design and event-related fMRI data, as measured by the average squared error. These results suggest that our proposed novel integrated approach will be useful for detecting brain functional activation in both block-design and event-related experimental fMRI data.

In this paper, Ag-Au alloy nanoparticles (NPs) were fabricated by dewetting process to enhance the broadband antireflection performance of textured silicon surfaces. The alloy NPs presented a large range of shapes and sizes, which provided an average reflectance (AR) below 4% over the spectral range of 300~1200 nm, a decrease of ~50% and ~90% as compared to the corresponding monometallic NPs and the original flat Si surfaces, respectively. The superior broadband antireflection demonstrated by the alloy NPs are attributed to the enhanced light trapping by alloy nanoparticle plasmon resonance.

An online automatic Ti-grade analyzer (OATGA), consisting of a (238)Pu source and a proportional counter, is designed for assuring product quality. The advantage of this system was its time-effectiveness; the entire monitoring process can be finished within approximately 5 min. This system has successfully solved some key analysis issues in titanium ore concentrate production process. The results obtained by feature measurements and chemical analysis were in agreement with each other. After two-year usage of OATGA at PANGANG, southwestern China, it was proved as a reliable method for online quality control in the production process of titanium ore concentrate., (Copyright 2009 Elsevier Ltd. All rights reserved.)