Search

The study systematically conducted orthogonal and univariate experiments to determine the optimal parameters for pyridine removal using vulcanized oil shale. The findings revealed that the key determinants of pyridine adsorption onto vulcanized oil shale included the pH level of the pyridine solution, contact time, initial pyridine concentration, and the quantity of vulcanized oil shale introduced into the system. Under optimal adsorption conditions, the vulcanized oil shale achieved a 48.00% adsorption efficiency and an equilibrium adsorption capacity of 4.83 mg·g−1. In contrast, the untreated oil shale exhibited a comparatively reduced adsorption efficiency of 22.88% under its respective optimal conditions. The research further examined the adsorption kinetics and isothermal adsorption models for both types of oil shale. The kinetic data indicated that pyridine adsorption followed a pseudo-second-order model. The pyridine adsorption isotherms for both oil shale and vulcanized oil shale were best described by the Freundlich model, compared to the Langmuir model. These findings provide significant insights into the utilization of vulcanized oil shale for the efficient treatment of low-concentration pyridine wastewater, potentially contributing to environmental remediation and pollution control strategies. [ABSTRACT FROM AUTHOR]

In inertial confinement fusion experiments, hot spot mix caused by hydrodynamic instabilities is a critical performance limitation. Currently, multi-channel Ross filter pair imaging is used to quantitatively diagnose the mix mass of cryogenic hot spots driven by 100 kJ energy, but this method brings significant uncertainty. To measure the level of mix more accurately, we have developed a two-temperature model to modify the fitted bremsstrahlung spectra based on the characteristics of cryogenic implosion hot spots. In experiments with good symmetry of hot spots, fitting analysis of the radial distribution of electron temperature was conducted. Compared with traditional single-temperature models, the two-temperature modeling method is more sensitive to the radial distribution of electron temperature and provides new diagnostic insights into the spatial distribution of cryogenic hot spot mix. [ABSTRACT FROM AUTHOR]

To address the issues of complex background in foreign object images, weak feature extraction, low detection accuracy for adhering small objects, and distortion in detection box positioning and scale, a foreign object detection algorithm for mining conveyor belts based on YOLOv5n-CND is proposed. First, the C2f module was used to optimize the feature pyramid, using fewer parameters to address the issue of poor sensitivity to small object detection caused by the complex background in foreign object images and interference from complex objects in underground environments. Second, the normalized Gaussian Wasserstein distance (NWD) regression loss function was used to replace CIoU, improving the performance of multi-scale foreign object detection and accurately predicting the detection of adhering small objects. Finally, a detection head (DyHead) was added, combining three attention mechanisms: scale, spatial, and task, to enhance feature extraction for foreign object contours and improve the adaptability to multi-scale targets. Experimental results demonstrated that YOLOv5n-CND achieved an mAP@0.5 of 87.9%, an mAP@0.5:0.95 of 55.9%, a parameter count of 4.49×106, and a detection speed of 85.5 frames per second, meeting the requirements for underground foreign object detection in coal mines. The mAP@0.5 and mAP@0.5:0.95 of YOLOv5n-CND were 2.6% and 3.4% higher than YOLOv5n, and 1.7% and 3.8% higher than YOLOv5s-CBAM, respectively. Although the model’s parameter count slightly increased compared to the YOLOv5n model, it was still lower than that of other models. Tests were conducted in four scenarios: foreign objects with elongated shapes resembling the background; anchor bolts with relatively low lighting; objects heavily mixed with coal gangue; and multiple foreign objects. The results indicated that the foreign object detection algorithm for mining conveyor belts based on YOLOv5n-CND did not result in false detections or duplicate detections, with very few missed detections. The detection box positioning was accurate, and the handling of adhering small objects was more effective, enabling precise detection of foreign objects on conveyor belts.

The Chern number is the core of topological photonics, which is used to describe the topological properties of photonic crystals and other optical systems to realize the functional transmission and the control of photons within materials. However, the calculation process of Chern numbers is complex and time-consuming. To address this issue, we use the deep learning accompanied with Maxwell's equations to predict the Chern number of a two-dimensional photonic crystal with a square lattice in this paper. We propose a numerical-to-image generative adversarial networks (GANs) augmentation method to solve the problem of insufficient training data. Our method demonstrates excellent predictive performance on the test dataset, achieving an average accuracy of 92.25%. Besides that, the proposed data augmentation method can significantly improve the accuracy of Chern number predictions by 7.95%, compared with the method that did not use this approach. This method offers what we believe to be a novel solution to the challenge of limited numerical data samples in deep learning applications like complex calculations of physical quantities. It may also have certain potential to improve deep learning algorithms in other fields of science and engineering.