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.

High Quantum Efficiency Rare-Earth-Doped Gd2O2S:Tb, F Scintillators for Cold Neutron Imaging.

High-resolution neutron radiography provides novel and stirring opportunities to investigate the structures of light elements encased by heavy elements. For this study, a series of Gd2O2S:Tb, F particles were prepared using a high-temperature solid phase method and then used as a scintillation screen. Upon reaching 293 nm excitation, a bright green emission originated from the Tb3+ luminescence center. The level of F doping affected the fluorescence intensity. When the F doping level was 8 mol%, the fluorescence intensity was at its highest. The absolute quantum yield of the synthesized particles reached as high as 77.21%. Gd2O2S:Tb, F particles were applied to the scintillation screen, showing a resolution on the neutron radiograph as high as 12 µm. These results suggest that the highly efficient Gd2O2S:Tb, F particles are promising scintillators for the purposes of cold neutron radiography.

Human expansion into Asian highlands in the 21st Century and its effects.

Most intensive human activities occur in lowlands. However, sporadic reports indicate that human activities are expanding in some Asian highlands. Here we investigate the expansions of human activities in highlands and their effects over Asia from 2000 to 2020 by combining earth observation data and socioeconomic data. We find that ∼23% of human activity expansions occur in Asian highlands and ∼76% of these expansions in highlands comes from ecological lands, reaching 95% in Southeast Asia. The expansions of human activities in highlands intensify habitat fragmentation and result in large ecological costs in low and lower-middle income countries, and they also support Asian developments. We estimate that cultivated land net growth in the Asian highlands contributed approximately 54% in preventing the net loss of the total cultivated land. Moreover, the growth of highland artificial surfaces may provide living and working spaces for ∼40 million people. Our findings suggest that highland developments hold dual effects and provide new insight for regional sustainable developments.

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.

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.