Improved Double Deep Q-Network Algorithm Applied to Multi-Dimensional Environment Path Planning of Hexapod Robots

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.

Redox-active phytic acid-based self-assembled hybrid material for enhanced uranium adsorption from highly acidic solution

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 Fe2+/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.

An efficiency function model of segmented gamma scanning for measuring radioactive waste drum

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 ¹³⁷Cs/⁶⁰Co 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.

An Anisotropic Velocity Model for Microseismic Events Localization in Tunnels

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.

A Novel Rapid-Flooding Approach With Real-Time Delay Compensation for Wireless-Sensor Network Time Synchronization

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.

Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM

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.

A survey of a COVID-19 cluster of charter flight importation

OBJECTIVES

Although a number of cases of importation with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been reported, there are still no data available concerning the characteristics in the coronavirus disease 2019 (COVID-19) cluster of charter flight importation. Here, we provide an analysis of COVID-19 cases and their close contacts who worked for the same company on a project in Karbala, Iraq, and returned back to Chengdu, China, by a charter flight.

METHODS

The data of imported COVID-19 cases and their close contacts were obtained from National Notifiable Disease Report System of Chinese Center for Disease Control and Prevention and field epidemiological investigation reports by Centers for Disease Control and Prevention (CDCs) in Chengdu. The information of general characteristics and laboratory findings of this cluster were collected and summarized.

RESULTS

One hundred and six (66.67%) of 159 charter flight passengers tested positive for COVID-19 before entry. Through treatment, all 159 people tested negative and meet the requirements of taking flights bound for China before boarding. However, there has been still 36 (22.64%) of them tested positive after entry. The median time from entry to confirmation was 1.0 day (Interquartile Range (IQR): 0-4.3). The Cycle threshold value (Ct value) of 36 patients' positive samples are all above 30 and most values are above 35.

CONCLUSIONS

In conclusion, there is still a risk that a number of COVID-19 cases can be imported through charter flight. However, the infectivity of confirmed patients of the charter flight was considered to be low.

Neural network method for localization of radioactive sources within a partially coded field-of-view in coded-aperture imaging

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.

Experimental research on a Raman-based distributed temperature sensor assisted by PCA for locating the temperature abnormal event of nuclear waste drums

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.

(Ce-Al)-oxide pillared bentonite: A high affinity sorbent for plutonium

The ability of bentonite and montmorillonite pillared by Al-oxide and mixed (Ln-Al)-oxides (Ln = La, Ce) to remove ²³⁹plutonium solution species from water is comparatively investigated at pH 7 and pH 4. Small-angle scattering and neutron contrast variation with H₂O/D₂O mixtures is used to verify the ingress of water in the calcined products after hydrophilicity was introduced by an NH₃-H₂O 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 >10⁴. XPS analysis of the (Ce-Al)-oxide pillared clays indicates the presence of Ce⁴⁺ as cerium dioxide. The progressive improvement in sorption performance in the order of pillar type Al₂O₃ < La₂O₃-Al₂O₃ << CeO₂-Al₂O₃ 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.

A genome-wide DNA methylation analysis in peripheral blood from patients identifies risk loci associated with Graves' orbitopathy

OBJECTIVE

Graves' orbitopathy (GO) is an inflammatory orbital disease of autoimmune origin with the potential to cause severe functional and psychosocial effects. The pathogenesis has not been fully elucidated. We investigated whether DNA methylation was associated with GO incidence in Chinese patients.

MATERIALS AND METHODS

Six GO patients and six age-matched controls were recruited, and genome-wide DNA methylation patterns were analyzed in their peripheral blood. t tests were performed to determine differential methylated sites in genomic regions and the univariable logistic regression analyses was performed to evaluate their risk with GO incidence. Cluster analysis and principal component analysis (PCA) were performed to determine the effects of the extracted differentially methylated sites.

RESULTS

One hundred and forty-eight differentially methylated sites were identified, including CD14 (fold change = 4.31, p = 0.005), IL17RE (fold change = 2.128, p = 0.005), and DRD4 (fold change = 0.25, p = 0.004), and were supported by cluster and PCA analyses. Univariable logistic regression analyses showed that the methylation patterns at 12 loci were associated with GO incidence. The relative risk per 1% decrease in methylation at ZCCHC6 and GLI3 was 0.15 (95% CI 0.03-0.91; p = 0.039) and 0.65 (95% CI 0.42-0.98; p = 0.042), respectively. Pearson correlation analyses demonstrated that methylation levels at IL17RE were positively associated with Clinical Activity Score (CAS) (r = 0.967, p < 0.05).

CONCLUSIONS

Our results demonstrate that differential methylation levels at analyzed sites (genes) may be risk markers of GO. DNA methylation analysis could provide new insights into understanding the disease and provide new treatment strategies for GO in Chinese patients.

DOUBLE-EXPONENTIAL FITTING FUNCTION FOR EVALUATION OF COSMIC-RAY-INDUCED NEUTRON FLUENCE RATE IN ARBITRARY LOCATIONS

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.

Heterostructured ZnFe2O4/Fe2TiO5/TiO2 Composite Nanotube Arrays with an Improved Photocatalysis Degradation Efficiency Under Simulated Sunlight Irradiation

To improve the visible light absorption and photocatalytic activity of titanium dioxide nanotube arrays (TONTAs), ZnFe₂O₄ (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/Fe₂TiO₅ (FTO)/TiO₂ composite nanotube arrays were successfully obtained. Furthermore, Fe³⁺ was reduced to Fe²⁺ 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.

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

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.

Fast adaptive particle spectrum fitting algorithm based on moment-estimated initial parameters

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.

Molecular characterization and phylogenetic analysis of porcine epidemic diarrhea virus samples obtained from farms in Gansu, China

Porcine epidemic diarrhea poses significant sanitation problems in the porcine industry, and has negatively affected the economy in recent years. In this study, 48 fecal specimens were collected from piglets from four intensive swine farms located in the Gansu Province of China. The molecular diversity and phylogenetic relationships between porcine epidemic diarrhea viruses (PEDV) prevalent in Gansu were probed, and the resultant proteins were characterized. Sequence analysis of the spike protein (S) genes showed that each specimen had unique characteristics, and that the PEDV1/S/4 strain could be differentiated from the others via a unique mutation of the S gene. The phylogeny of S glycoprotein showed that all strains were clustered into two major groups. The four Gansu PEDV field strains were characterized into different groups; this finding was consistent with the results of the protein characterization prediction. This analysis additionally revealed the unique characteristics of each specimen. The results of this study could be used to elucidate the prevalence of PEDV and contribute to the prevention of PEDV in Gansu.

Analysis of FMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach

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.

Alloy nanoparticle plasmon resonance for enhancing broadband antireflection of laser-textured silicon surfaces

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.