Evaluation of CBR of Graded Crushed Stone of Flexible Base Structural Layer Based on Discrete Element Model

In order to study the mechanical properties of graded crushed stone, the discrete element method is used to simulate the CBR test of graded crushed stone. Aiming at the composition structure of graded crushed stone material, the PFC3D simulation software is used to construct the test model, and the process of constructing the virtual specimen model of the graded crushed stone discrete element model is discussed in detail. A servo mechanism is used to control the speed of the wall in the software, so as to control the virtual confining pressure imposed on graded crushed stone by the wall and simulate the real CBR test environment. The micro-parameter calibration of the virtual test is carried out by comparing the indoor and virtual CBR specimens of a single particle size specimen and three groups of graded crushed stone specimens. The comparison result shows that the stress–strain characteristics of the graded crushed rock obtained by the discrete element simulation during the uniaxial penetration process have a high degree of similarity, which can verify the accuracy of the model establishment. With the increase in the penetration depth, the penetration force of the aggregates of various particle sizes gradually increases, and the penetration force and the penetration depth are basically linear, and when the particle size is greater than 9.5 mm, the increase in particle size has little effect on the CBR test results. Under the certain conditions, the contact stiffness of graded crushed stone particles with particle sizes of 4.75 mm, 9.5 mm, 13.2 mm, 16 mm, and 19 mm should be 0.88 × 107 (N/m), 0.98 × 107 (N/m), 1.10 × 107 (N/m), 1.25 × 107 (N/m), and 2.05 × 107 (N/m), respectively. The recommended value of the contact stiffness of the small spherical particles increases with the increase in the particle size. This model can provide a basis for studying the micromechanical state of graded crushed stone and physical mechanics tests

Crystal structure and microwave dielectric properties of Li-modified BaSi2O5 ceramics

Ba1-xLixSi2O5-x/2 (x = 0–0.5) ceramics were prepared by solid-state reaction method. The occupation tendency of Li+ was well explained based on First-principle calculation and unit cell volume changes. Li+ non-equivalent substitution for Ba2+ considerably reduced the sintering temperature to 975 °C, in comparison with 1225 °C for pure BaSi2O5. Single phase solid solution with orthorhombic structure was observed with x = 0–0.1. Nevertheless, trace amounts of unexpected secondary phases Li2Si2O5 and SiO2 were detected for higher Li-containing samples. Notably, the Q×f significantly enhanced up to 58.16% from 16,134 GHz (x = 0) to 25,518 GHz (x = 0.01), which predominantly depended on the structural characteristics, such as packing fraction and bond covalence. Increase x from 0 to 0.5 led to the reduction of dielectric constant εr from 7.11 to 5.95. The resonant frequency temperature coefficient τf was directly dominated by oxygen bond valence (VO), rather than VBa and VSi. Ba0.99Li0.01Si2O4.995 ceramic sintered at 1050 °C for 4 h exhibited a high Q×f value of 25,518 GHz, and the εr and τf values were 6.94 and −29.46 ppm/°C, respectively

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

Abstract This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman’s correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19