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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]

Neutron imaging is an effective nondestructive testing (NDT) technique widely applied to detect structural defects and the enrichment of nuclear fuel elements due to its high penetration and nuclide-sensitive properties. Since the fuel element pellet is sealed in the cladding, the transmission imaging result is a superposition of the two parts. Therefore, the attenuation of neutrons by the cladding is interference that must be considered in the enrichment analysis. It is necessary to extract and separate cladding and pellets using an edge extraction method. However, the low neutron cross-section of the cladding material (e.g., aluminum and zirconium) leads to poor grayscale contrast at the cladding edge in the imaging result, and the intensity of the cladding edge is significantly lower than that of the pellet edge. In addition, affected by the noise from the imaging environment, the boundaries of targets are further blurred, making edge detection more challenging. Traditional detection algorithms extract the weak edges of cladding incompletely, and the results are discontinuous, with obvious edge breaks and missing areas. This paper proposes a method to extract edges in neutron images based on phase congruency (PC). This study utilized the classical perceptual field model to improve contrast at weak edges. The enriched edge map was generated using our PC model from six directions, allowing more weak edges to be detected accurately. The non-maximum suppression ensured precise localization and avoided edge breaks. Furthermore, the edge results were optimized by eliminating noise through morphological operations. The experimental results demonstrate that the proposed method effectively detects the weak edges of the cladding, is superior in accuracy and integrity to traditional detection, and is able to obtain stable and reliable results with different materials of neutron images. The edge integrity improved by 64.1%, and the edge localization accuracy reached 94.3%. The extracted edge information is useful in the next stage of the high-precision enrichment analysis. [ABSTRACT FROM AUTHOR]

For this study, ion-eroded cement was prepared from the cement required for construction of middle- and low-level radioactive waste repositories in caves. The properties for adsorption of 137Cs on cement before and after ion erosion were investigated. XRF, XRD, SEM–EDS and BET were used to analyse various cement materials. The effects of reaction time, solid–liquid ratio, initial radioactivity and different ions on adsorption were studied by static batch experiments. When the initial radioactivity was 1114.5 Bq L−1 and the solid–liquid ratio was 5 g L−1, the adsorption equilibrium time was 12 h. Adsorption of 137Cs on ion-eroded cement was more effective than that on untreated cement. The adsorption process was consistent with the pseudosecond-order kinetic model and the Freundlich isotherm model, and the process involved multilayer chemisorption. This study provides basic research data for construction of a disposal repository. [ABSTRACT FROM AUTHOR]

Identifying useful microseismic events is one of the key steps in monitoring tunnel rockbursts. Here, we propose a strong noise-tolerance deep learning (SNTDL) network for the automatic classification of noisy microseismic events. The training set, validation set, and test set of the SNTDL network consist of 27989 unfiltered microseismic recordings. First, to comprehensively characterize the microseismic events, we extract ten weakly correlated features of the microseismic recordings as the input of the SNTDL network. Then, the skip connection and concatenation structure are added to this network, which can further enhances its generalization ability. Additionally, the SNTDL, AlexNet, Inception, visual geometry group, and ResNet are compared using the synthetic microseismic recordings with different signal-noise ratios. The results demonstrate that the SNTDL network has a higher accuracy and stronger noise-tolerance capability than the other approaches. Application to a dataset collected from a different construction environment confirms that the SNTDL network can still achieve an accurate classification result, which further verifies that the proposed network has a reliable generalization performance. [ABSTRACT FROM AUTHOR]

In our previous work, a new design for a gaseous neutron detector composed of a boron-lined multilayer converter was proposed to replace the 3He proportional counter, and a prototype detector was constructed to test its performance and characteristics. The neutron detection efficiency varies for different incident angles of neutrons to the converter surface, mainly due to the effective area of the converter and the emitting probability of the alpha and 7Li. Hence, the variation of detection efficiency with the incident angle is analyzed theoretically in this work, and then the GEANT4 simulations and preliminary experimental tests are conducted to verify the calculation results. The highest detection efficiency is obtained at 1.72° (100 mm length, 3 mm gap, and θ = atan (3/100) ≈ 1.72°), the detection efficiency decreases sharply when the angle approaches 0°, and the detection efficiency decreases gradually when the incident angle is larger than 1.72°. The simulation results and experimental results are consistent with the trend of theoretical calculation. Therefore, the proposed detector is suitable for angle-sensitive applications, such as small angle neutron scattering and locating the isotopic neutron sources. [ABSTRACT FROM AUTHOR]

In digital nuclear spectroscopy, trapezoidal shaping is widely used. Compared with traditional CR-RC4 semi-Gaussian shaping, it has a better energy resolution and higher counting rates, but does not void the pulse pile-up in the case of extreme counting rates. In this paper, a new recursive algorithm is proposed that can improve the anti-pile-up ability, and is easy to implement in any DSP-based processor that is used in any digital pulse shaping filter section. The complete deduction and simulation are presented in this paper. [ABSTRACT FROM AUTHOR]

The adsorption behavior and mechanism of Cs+ on surrounding granite of nuclear waste repository under the groundwater environment were investigated by batch experiments and characterization methods. Results indicate that the adsorption under groundwater environment is inhibited for the presence of other ions, and the adsorbed Cs+ by crushed granite are distributed in the area where K+ is deficient, proving that Cs+ can be captured by granite through ion exchange with K+. This study can provide a key reference for the safety assessment of the repository. [ABSTRACT FROM AUTHOR]

The preparation of C30 concrete for a cavernous waste repository under construction in China and its Cs(I) adsorption performance were investigated. The mix ratio of C30 concrete was cement:sand:gravel:water = 1:1.87:3.05:0.55. Three characterization analyses showed that C30 concrete has a good crystal structure, a large specific surface area and the presence of many –OH, HCO3−, CO32−, SiO42− groups and other groups. Adsorption experiments showed that the adsorption equilibrium was reached within 4 h, and pH had little effect on adsorption; several ions (K+, Ca2+, Mg2+, Cl−, NO3−, and SO42−) inhibited adsorption. The adsorption was more in line with the pseudo-second-order kinetic model and the Langmuir isothermal model. Thermodynamic analysis indicates that adsorption is endothermic and stochastic. [ABSTRACT FROM AUTHOR]

Groundwater is the most important factor contributing to the diffusion and migration of radionuclides in the repository. In this paper, the characteristic parameters of granite in a repository are obtained from static and dynamic experiments to simulate a three-dimensional geological model of the repository with the help of a groundwater modeling system (GMS). It was shown that the longest distances of 137Cs and 60Co migrations for 500 a were 146.66 m and 145.04 m, respectively. This work could be of great significance to ensure the long-term safe operation of this type of waste repository. [ABSTRACT FROM AUTHOR]

Cs2LiYCl6: Ce3+ (CLYC) is a dual-mode gamma-neutron scintillator with a medium gamma-ray resolution and pulse-shape discrimination (PSD) capability. The PSD performance of CLYC is greatly weakened when coupled with silicon photomultipliers (SiPMs) because of SiPMs' low detection efficiency for the ultrafast Core-Valence-Luminescence (CVL) component under gamma excitation. In our previous work, the PSD Figure-of-Merit (FoM) value was optimized to 2.45 at the gamma-equivalent energy region of the thermal neutron by using the charge comparison method. However, this value was reduced to 1.37 at the lower gamma-equivalent energy region of more than 325 keV, and neutrons were difficult to distinguish from gamma rays. Hence, new algorithms should be studied to improve the PSD performance at low gamma-equivalent energy regions. Convolutional Neural Networks (CNNs) have excellent image recognition capabilities, and thus, neutron and gamma-ray waveforms can be discriminated by their characteristics through a known training set. In this study, neutron and gamma-ray waveforms were measured with a 137Cs source and moderated 252Cf source via an SiPM array-coupled CLYC detector and divided into two groups: training and PSD testing. The CNN training set comprised 137Cs characteristic gamma-ray waveforms and thermal neutron waveforms that were discriminated by the charge comparison method from the training group. A CNN with two convolution-pooling layers was designed to accomplish PSD with the test group. The PSD FoM value of the CNN method was calculated to be 37.20 at the gamma-equivalent energy region of more than 325 keV. This result was much higher than that of the charge comparison method, indicating that neutrons and gamma rays could be better distinguished with the CNN method, especially at low gamma-equivalent energy regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper discusses the attitude cooperative control of multiple unmanned aerial vehicle systems (MUAVs) with unknown dynamics and external disturbances. Distributed fast nonsingular terminal sliding mode control (FNTSMC) is used with quaternion-description dynamical systems. The dynamics and external disturbances are changed into lumped disturbances by formula transformation. A robust nonlinear disturbance observer is proposed to estimate the lumped disturbances in finite time. Then, combining the fast terminal sliding mode control, distributed FNTSMC controllers are designed under the directed topology. Based on the Lyapunov stability theory and graph theory, convergence stability of the nonlinear systems is strictly proved, and the tracking errors between the leader and the followers approach to a small residual set. Finally, the simulation example is presented to illustrate the effectiveness and advantage of the proposed controllers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Bentonite was irradiated with 60Co γ-rays at a dose rate of 10 kGy/h to achieve irradiation doses of 1, 2, 3 MGy and was characterized to evaluate its stability before and after irradiation and understand the radiation damage mechanism. The structure of the bentonite was slightly affected by high doses of irradiation, and the U(VI) adsorption capacity of the irradiated bentonite was reduced compared to that of raw bentonite. Overall, this study provides insights into the negative effects of high-dose γ-ray irradiation on the stability and adsorption capacity of bentonite, which has safety implications for the storage of radioactive waste. [ABSTRACT FROM AUTHOR]

High spatial resolution tomographic gamma scanning (TGS) reconstruction is very important for the radioassay of drummed low-level radioactive waste. High spatial resolution means that the divided voxels are finer. Due to the large size of the drum, the traditional image reconstruction method based on complete samples takes a long time to scan. To limit the scanning time of the drum, sparse sampling is required. The maximum likelihood expectation maximization (MLEM) is widely used in TGS image reconstruction from projection data, but for high spatial resolution TGS imaging, its quality is insufficient to accurately describe the media boundary and determine radioactivity. The improved MLEM algorithm based on total variation (TV) regularization, such as the MLEM- TV minimization (TVM) algorithm, has been applied to reconstruct high spatial resolution TGS images. The split Bregman algorithm can quickly solve the partial differential equations of TV regularization. In this work, the split Bregman anisotropic TV (SBATV) and the split Bregman isotropic TV (SBITV) are the first time adopted to improve the iterative process of the MLEM algorithm, which are MLEM- SBATV and MLEM- SBITV. Experimental results show that both the MLEM- SBATV algorithm and the MLEM- SBITV algorithm can accurately reconstruct high spatial resolution TGS transmission images with sparse sampling. The MLEM- SBITV algorithm performs better in reconstructing the TGS emission images from sparse sampling than the traditional MLEM, MLEM- TVM, and MLEM- SBATV algorithms, increasing radionuclide positioning and radioactivity reconstruction accuracy. [ABSTRACT FROM AUTHOR]

An organic compound adsorbent is prepared by modifying acid-activated bentonite with cetyltrimethylammonium bromide (CTAB). The physical and chemical properties of the samples before and after modification are characterized by X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM) and zeta potential analysis. A batch static adsorption experiment is used to study the adsorption properties of Cs+ before and after modification. Results show that the times to reach the adsorption equilibrium of bentonite before and after modification are 12 h and 2 h, respectively, and the adsorption rates are 26% and 85%, respectively. Different types of ions have different effects on adsorption. Pseudo-first-order and pseudo-second-order kinetic simulations and adsorption isotherm simulations are carried out with the data, and the adsorption capacities of the two adsorbents for Cs+ are 4.8 mg/g and 22.6 mg/g, respectively. The calculated thermodynamic parameters show that the adsorption is endothermic and spontaneous. [ABSTRACT FROM AUTHOR]

A kind of amino-functionalized clay was prepared by grafting APTES from clay in the Alxa area (Inner Mongolia, China), and its application for the U(VI) removal from Alxa groundwater was studied in this work. The results indicate that the sorption strongly depended on the pH and ions. The presence of amino functional groups significantly enhances the adsorption of uranium on clay in groundwater. The sorption isotherms fitted well with the Langmuir model, whereas the sorption kinetics could be fitted by a pseudo-second-order model. These findings indicate that amino-functionalized clay can effectively adsorb U(VI) in complex groundwater samples. [ABSTRACT FROM AUTHOR]

Alpha energy spectrum measurement has been employed in the nuclear waste disposal of transuranic nuclides (such as 239Pu and 241Am), supervision, and disposal process. The alpha spectrum is made up of alpha particles, which have a fast-moving helium nucleus and an energy of 4–8 MeV with weak penetration ability. Removing alpha particles from radioactive nuclides is an important scientific issue. In this study, a transuranic nuclide alpha particle pulse generator that produces simulated alpha particle pulses similar to real particles was designed. Field programmable gate array (FPGA) was adopted as its core chip and we obtained the digital pulse waveform using software tracing points while simulating real alpha particles by random numbers. Accordingly, the alpha energy spectrum of a radioactive source 241Am was obtained using a passivated ion-implanted planar silicon (PIPS) detector. Afterward, the alpha particle was extracted from the energy spectrum and was then compared to the alpha particle pulse of the two methods, deriving a result. Here, both groupings of particle pulse waveforms were found to be very similar, and the periodic error of the particle was observed to be less than 1%. Furthermore, the amplitude and time interval of the particle were apparently similar to the actual spectrometry pulse. [ABSTRACT FROM AUTHOR]

Clock skew compensation is essential for accurate time synchronization in wireless networks. However, contemporary clock skew estimation is based on inaccurate transmission time measurement, which makes credible estimation challenging. Based on one-way broadcast synchronization, this article presents a novel maximum likelihood estimation (MLE) with an innovative implementation to minimize the clock skew estimation error caused by delay. A multiple one-way broadcast model is developed for observation and set collection, while the distribution of actual delay is discussed. A clock skew MLE based on Gaussian delay is then proposed and its Cramer–Rao lower bound provided. The developed MLE is independent of the clock offset estimation, and requires only two synchronization periods to generate an accurate estimation. Extensive experimental results indicate that the performance of the proposed MLE is better than the methods popularly used in existing time synchronization protocols. [ABSTRACT FROM AUTHOR]

Montmorillonite colloid was synthesized and characterized and the adsorption of U(VI) on colloid as a function of contact time, temperature, initial concentration of U(VI) solution, pH and foreign ions was investigated through batch experiments. The results illustrate that the adsorption reaches equilibrium quickly within 30 mins. Results showed that the absorption process is influenced by pH value, temperature and ions, among which pH value is the most significant. Adsorption percentage increases obviously at pH = 4.0–7.0, and then decreases with the increase of pH value. The competitive cations also inhibit the adsorption. pH value and ions affect the surface properties and charges of the colloid and the chemical form of nuclides. The experimental data was analyzed in detail. The maximum adsorption capacity obtained by fitting the second-order kinetic model is very close to the experimental data. The Langmuir model fits the experimental data better than Freundlich models, R2 = 0.991. The results of fitting two adsorption isotherms show that the adsorption of colloids on uranium has both physical and chemical adsorption, and is mainly monolayer adsorption. Furthermore, the adsorption process is a spontaneous endothermic reaction by calculating the thermodynamic parameters. This experiment can provide some data basis for the adsorption study of U(VI) on colloids. [ABSTRACT FROM AUTHOR]

Currently, the frequency of landslides is increasing. Scientific monitoring methods are playing an essential role in effectively reducing landslide disasters. This paper proposes a method for identifying the cracks at the trailing edge of a landslide (TEL) based on image processing technology and adopts the custom interval median comparison algorithm (IMCA) to calculate the crack motion parameters. First, we perform a series of processes on the TEL images, including image preprocessing, Otsu's algorithm processing, and Canny edge detection processing, to identify the outline of the TEL. Then, we propose using the azimuth and displacement to characterize the motion of the cracks and using the IMCA to calculate the changes before and after motion of any two groups of cracks. Finally, we design a computer program using a free and open-source widget toolkit (named QT platform) based on the calculation model that corresponds to the proposed method, and we apply the crack monitoring test to a 3D simulation model, a gravel model, a soil model, and a collapsed body of the Panzhihua Airport landslide in southwestern China. From the results, it can be assessed that the method can identify the outline of the TEL and calculate the azimuth and displacement of two crack curves before and after motion. These two parameters can describe the movement of the trailing edge cracks of the monitored landslide. Thus, this method can be used in early warning system for landslide hazards. [ABSTRACT FROM AUTHOR]

Accurate and fast-convergent time synchronization is very important for wireless sensor networks. The flooding time synchronization converges fast, but its transmission delay and by-hop error accumulation seriously reduce the synchronization accuracy. In this article, a rapid-flooding multiple one-way broadcast time-synchronization (RMTS) protocol for large-scale wireless sensor networks is proposed. To minimize the by-hop error accumulation, the RMTS uses maximum likelihood estimations for clock skew estimation and clock offset estimation, and quickly shares the estimations among the networks. As a result, the synchronization error resulting from delays is greatly reduced, while faster convergence and higher-accuracy synchronization is achieved. Extensive experimental results demonstrate that, even over 24-hops networks, the RMTS is able to build accurate synchronization at the third synchronization period, and moreover, the by-hop error accumulation is slower when the network diameter increases. [ABSTRACT FROM AUTHOR]

Average consensus theory is intensely popular for building time synchronization in wireless sensor network (WSN). However, the average consensus-based time synchronization algorithm is based on the iteration that poses challenges for efficiency, as they entail high communication cost and long convergence time in large-scale WSN. Based on the suggestion that the greater the algebraic connectivity the faster the convergence, a novel multihop average consensus time synchronization (MACTS) is developed with innovative implementation in this article. By employing multihop communication model, it shows that virtual communication links among multihop nodes are generated and algebraic connectivity of the network increases. Meanwhile, a multihop controller is developed to balance the convergence time, accuracy, and communication complexity. Moreover, the accurate relative clock offset estimation is yielded by delay compensation. Implementing the MACTS based on the popular one-way broadcast model and taking multihop over short distances, we achieve hundreds of times the MACTS convergence rate compared to average TimeSync (ATS). [ABSTRACT FROM AUTHOR]

Different from the regular method with peak-shape function fitting, in this work, the idea of alpha-spectra analysis using non-negative iterative deconvolution method is proposed. Two non-negative iterative algorithms, the Boosted Gold and the Boosted Richardson–Lucy, were applied to unfold and analyze alpha-particle spectra. The detector response matrix was constructed with the AASI, which is a Monte Carlo simulation software specifically for alpha-particle spectra. A 244 Cm alpha-particle source was measured and tested for peak-position deconvolution. A 238 + 239 + 240 Pu alpha-particle spectrum was used to prove the validity of quantitative analysis by deconvolution algorithms. Both the Boosted Gold and the Boosted Richardson–Lucy can get accurate results for peak positions and content ratios. In terms of running speed, the Boosted Gold was faster than the Boosted Richardson–Lucy. [ABSTRACT FROM AUTHOR]

Aiming at high performance requirements of snake-like robots under complex environment, we present a control system of our proposed design which utilizes a STM32 as the core processor and incorporates real-time image acquisition, multisensor fusion, and wireless communication technology. We use Solidworks to optimize the design of head, body, and tail joint structure of the snake-like robot. The system is a real-time system with a simple-circuit structure and multidegrees of freedom are attributed to the flawless design of control system and mechanical structure. We propose a control method based on our simplified CPG model. Meanwhile, we improve Serpenoid control function and then investigate how different parameters affect the motion gait in terms of ADAMS emulation. Finally, experimental results show that the snake-like robot can tackle challenging problems including multi-information acquisition and processing, multigait stability, and autonomous motion and further verify the reliability and accuracy of the system in our combinatory experiments. [ABSTRACT FROM AUTHOR]

Attitude determination is a significant aspect of the navigation technology. The determination of the attitudes based on accelerometer and magnetometer fusion is basically an applied mathematical problem. In this article, we obtained the proposed quaternionic symbolic solution for accelerometer and magnetometer fusion which is quite different from existing methods. The proposed symbolic solution has the advantages of high accuracy and consumes much less time. Besides, the normalization problem of input data was discussed. Biquaternions ware introduced to describe the existence of the complex quaternion. Several experiments were carried out to verify the proposed symbolic solution. [ABSTRACT FROM AUTHOR]

A Kalman-filter-based method is proposed to address challenges in nuclear signal processing, including pileup events and noise cancelation. The state-transition equation is established on the basis of suitable pulse shape models. By regarding the output signal of particle detectors as a measurable variable, the measurement equation is also introduced to correct dynamic processes of the state transition. A linear Kalman filter with $n$ tunnels is constructed to separate pileup events and maintain the original waveforms of pulses. In addition, two terms are defined and addressed for separation operations, namely, Availability, which is used to prepare for upcoming events, and Clearance Mechanism, which is given to remove thumbnail information from the previous events and make occupied tunnels available to subsequent nuclear events. Simulation and field experiments demonstrate that the proposed method can handle critical situations of pulse pileup and minimize count loss and dead time in the X-ray energy spectrum while maintaining the original pulse shapes. [ABSTRACT FROM AUTHOR]

First arrival picking is a key factor which affects the precision of microseismic data analysis. Here, we propose a new method, which employs the maximum eigenvalue to constraint the Maeda-Akaike Information Criterion (Maeda-AIC) algorithm. First, aims at addressing the pick result affected by signal-to-noise ratio (SNR) of microseismic data, maximum eigenvalue method based on polarization analysis is applied, and the maximum eigenvalue is calculated firstly, as for three component (3C) microseismic data, the maximum eigenvalue is calculated with corresponding covariance matrix, a time window need to be set in the process of building the covariance matrix, and it is the only time window set in the method proposed in this paper, so the method is called single window Maeda-AIC (SWM-AIC), to the single component (1C) microseismic data, the variance of the data is taken as the maximum eigenvalue. Then, to reduce the effect of time window and increase the automation of the algorithm, Maeda-AIC method which is a non-window-based first arrival picking method is applied. Maeda-AIC values in preliminary window are calculated, and the preliminary window is the sequence before the largest eigenvalue of the 3C or 1C data. We validate the developed method with both synthetic and field microseismic data, using a range of signal-to-noise ratios. The developed method is compared with some basic methods, specifically STA/LTA, Maeda-AIC, and the maximum eigenvalue method. The results demonstrate that the new method is much better at identifying first arrival times than basic methods when the data have a low signal-to-noise ratio, and is even faster than the STA/LTA method with 1C data. In contrast to other improved methods, threshold value is not required for this method, and the only time window used in this method is just for maximum eigenvalue calculation, through test in the paper, its length has almost no effect on the first arrival picking. [ABSTRACT FROM AUTHOR]

The cyclotron-wave rectifier can efficiently convert microwaves into direct current in wireless power transmission. Its resonator operates in the quasi-H111 mode, where there is field non-uniformity; how this field non-uniformity will affect the energy conversion is determined. In this paper, through Computer Simulation Technology simulation, combining experiential analysis with numerical fitting obtains the field expression of the operating mode, approximately calculates the Coulomb field by using the model of a filamentary electron beam, and then through the numerical solution, analyzes the effect of the field non-uniformity on the energy conversion efficiency in both the resonator and the conversion region. The results demonstrate that when the relationship between the beam current I0 and the beam radius robr satisfies the Brillouin relationship, a lesser current density can maintain the beam shape very well and ensure a high beam-wave interaction efficiency; furthermore, in a reverse magnetic field region, when I0 ≤ 0.75Iobr, a higher energy conversion efficiency can be predicted. What is more, the conclusion is in agreement with particle-in-cell simulation. Finally, numerical computation also indicates that high beam-wave interaction efficiency requires a smaller radius of the electron beam. [ABSTRACT FROM AUTHOR]

The aim of this study is to characterize plutonium sorption particularly Pu distribution coefficients and the influence of pH on sorption processes in saturated geological materials used as a radioactive waste repository in China. The examined geological media is from a southwestern Chinese repository and consists of soil and slates. The backfill material consists of Na-bentonite from Inner Mongolia Gaomiaozi, and will be used at the Beishan high-level waste (HLW) repository, Gansu Province. The results show that the sorption capacity of Pu by geological medium in an acidic environment is low, but significantly increases with increasing pH. [ABSTRACT FROM AUTHOR]

A new statistical fitting approach, named Statistical Distribution-Based Analytic (SDA) method, is proposed to fit single Gaussian-shaped K and K X-ray peaks recorded by Si(PIN) and silicon drift detector (SDD). In this method, we use the discrete distribution theory to calculate standard deviation of energy resolution σ. The calibration of σ and energy ( E) for two detectors between the energy ranges of 4.5-26 keV are also completed by measuring characteristic X-ray spectra of nineteen types of pure elements. With the spectrum fraction (SF) parameter proposed in this paper, the SDA method can be used to resolve overlapping peaks. In measured spectra, the Gaussian part of X-ray peaks can be fitted by a Gaussian function with two parameters, σ and SF. This new fitting approach is simpler than traditional methods and it achieves relatively good results when fitting the complex X-ray spectra of national standard alloy samples detected by Si(PIN) and SDD detectors. The χ values are obtained for each spectrum to assess fitting results, and the SDA fitting method gives a preferable fit for the SDD detector. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a novel digital predistortion method for both memory and memoryless power amplifiers, which only needs one feedback circuit. In comparison with the previous methods, the proposed method has the greatest advantage that it can eliminate the gain and phase imbalance errors of the quadrature demodulator without digital IQ compensator, and the performance of digital predistortion can be improved with lower costs and less computation complexity. Combining with the indirect learning structure, it is easy to design the predistorter. Experimental results show the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

In this article totally asymmetric simple exclusion process (TASEP) with constrained hopping and parallel dynamics at a junction is investigated using a mean-field approximation and Monte Carlo simulations. The constrained particle hopping probability r (r ≤ 1) at a junction may correspond to a delay caused by a driver choosing the right direction or a delay waiting for green traffic light in the real world. There are six stationary phases in the system, which can reflect free flow and congested traffic situations. Correlations at the junction point are investigated via simulations. It is observed that small r leads to stronger correlations. The theoretical results are agreement with computer simulations well. [ABSTRACT FROM AUTHOR]