58 research outputs found
The force and dynamic response of low-velocity projectile impact into 3D dense wet granular media
The presence of liquid bridges between particles in a wet granular media gives rise to capillary forces at the microscopic scale that dramatically alters the material's microstructure and macroscopic responses. Previous experimental studies show significant differences in the penetration depth of the projectile into assemblies of dry or wet particles. Still, there is a lack of fundamental understanding of the difference induced by the presence of liquid bridges in dynamic regimes. In this study, the contact model parameters of wet glass beads are calibrated employing the angle of repose tests and cylinder lifting tests, and a series of three-dimensional discrete element method simulations of spheres impact into wet granular packings are conducted. The dynamic characteristics of the projectile are obtained numerically and found to be in good agreement with the experimental data. The analyses indicate that the final penetration depth in the wet granular media is linearly related to the initial velocity and has a power function relationship with the impact energy. The generalized Poncelet law is suitable for wet granular materials, but the presence of liquid significantly affects the values of the parameters. There is a velocity “stagnation zone” at the initial stage of the wet granular impact, and the final penetration depth and the duration interaction time are smaller than that of the dry case. The difference can be attributed to the resistance evolution of the projectile exerted by particles and gives the parameter scaling law of the peak impact force. Further, a macro-micro transition technique is employed to characterize the velocity field, pressure field, and stress field inside the wet granular media and the spatial distribution is quantified based on stress theory.</p
A SARS-CoV-2 and influenza double hit vaccine based on RBD-conjugated inactivated influenza A virus
The circulating flu viruses merging with the ongoing COVID-19 pandemic raises a more severe threat that promotes the infectivity of SARS-CoV-2 associated with higher mortality rates. Here, we conjugated recombinant receptor binding domain (RBD) of SARS-CoV-2 spike protein onto inactivated influenza A virus (Flu) to develop a SARS-CoV-2 virus-like particle (VLP) vaccine with two-hit protection. This double-hit vaccine (Flu-RBD) not only induced protective immunities against SARS-CoV-2 but also remained functional as a flu vaccine. The Flu core improved the retention and distribution of Flu-RBD vaccine in the draining lymph nodes, with enhanced immunogenicity. In a hamster model of live SARS-CoV-2 infection, two doses of Flu-RBD efficiently protected animals against viral infection. Furthermore, Flu-RBD VLP elicited a strong neutralization activity against both SARS-CoV-2 Delta pseudovirus and wild-type influenza A H1N1 inactivated virus in mice. Overall, the Flu-RBD VLP vaccine is a promising candidate for combating COVID-19, influenza A, and coinfection
Optical bulk-boundary dichotomy in a quantum spin Hall insulator
The bulk-boundary correspondence is a key concept in topological quantum
materials. For instance, a quantum spin Hall insulator features a bulk
insulating gap with gapless helical boundary states protected by the underlying
Z2 topology. However, the bulk-boundary dichotomy and distinction are rarely
explored in optical experiments, which can provide unique information about
topological charge carriers beyond transport and electronic spectroscopy
techniques. Here, we utilize mid-infrared absorption micro-spectroscopy and
pump-probe micro-spectroscopy to elucidate the bulk-boundary optical responses
of Bi4Br4, a recently discovered room-temperature quantum spin Hall insulator.
Benefiting from the low energy of infrared photons and the high spatial
resolution, we unambiguously resolve a strong absorption from the boundary
states while the bulk absorption is suppressed by its insulating gap. Moreover,
the boundary absorption exhibits a strong polarization anisotropy, consistent
with the one-dimensional nature of the topological boundary states. Our
infrared pump-probe microscopy further measures a substantially increased
carrier lifetime for the boundary states, which reaches one nanosecond scale.
The nanosecond lifetime is about one to two orders longer than that of most
topological materials and can be attributed to the linear dispersion nature of
the helical boundary states. Our findings demonstrate the optical bulk-boundary
dichotomy in a topological material and provide a proof-of-principal
methodology for studying topological optoelectronics.Comment: 26 pages, 4 figure
A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones
Analysis of the Influence of Parameters of a Spraying System Designed for UAV Application on the Spraying Quality Based on Box–Behnken Response Surface Method
With the development of precision agriculture (PA), low-altitude and low-volume spraying based on unmanned aerial vehicles (UAVs) is playing an increasingly important role in the control of crop diseases, pests, and weeds. However, the aerial spraying quality and droplet drift are affected by many factors, some of which are controllable (e.g., flight and spraying parameters) and some of which are not (e.g., environmental parameters). In order to study the influence of spraying parameters on the UAV-based spraying performance, we propose a UAV-compatible spraying system and a customized experimental platform in this work. Through single-factor test and Box–Behnken response surface methods, four influencing factors, namely spraying height, flow rate, distance between nozzles, and pulse width modulation (PWM) duty cycle, were studied under indoor conditions. Variance analysis and multiple quadratic regression fitting were performed on the test data by using Design-Expert 8.0.5B software, and quadratic polynomial regression models of effective spraying width, droplet coverage density, coefficient of variation, and droplet coverage rate were established. Based on the Z-score standardization, a mathematical model of the comprehensive score with four factors was established to evaluate the spraying quality and predict optimal spraying parameters. Test results indicate that the effect intensity of four influencing factors from strong to weak is PWM duty cycle, flow rate, distance between nozzles, and spraying height, and their optimal values are 98.65%, 1.74 L/min, 1.0 m, and 1.60 m, respectively. Additionally, verification experimental results demonstrate that the deviation between the predicted comprehensive score and the actual value was less than 6%. This paper can provide a reference for the design and optimization of UAV spraying systems
Analysis of the Influence of Parameters of a Spraying System Designed for UAV Application on the Spraying Quality Based on Box–Behnken Response Surface Method
With the development of precision agriculture (PA), low-altitude and low-volume spraying based on unmanned aerial vehicles (UAVs) is playing an increasingly important role in the control of crop diseases, pests, and weeds. However, the aerial spraying quality and droplet drift are affected by many factors, some of which are controllable (e.g., flight and spraying parameters) and some of which are not (e.g., environmental parameters). In order to study the influence of spraying parameters on the UAV-based spraying performance, we propose a UAV-compatible spraying system and a customized experimental platform in this work. Through single-factor test and Box–Behnken response surface methods, four influencing factors, namely spraying height, flow rate, distance between nozzles, and pulse width modulation (PWM) duty cycle, were studied under indoor conditions. Variance analysis and multiple quadratic regression fitting were performed on the test data by using Design-Expert 8.0.5B software, and quadratic polynomial regression models of effective spraying width, droplet coverage density, coefficient of variation, and droplet coverage rate were established. Based on the Z-score standardization, a mathematical model of the comprehensive score with four factors was established to evaluate the spraying quality and predict optimal spraying parameters. Test results indicate that the effect intensity of four influencing factors from strong to weak is PWM duty cycle, flow rate, distance between nozzles, and spraying height, and their optimal values are 98.65%, 1.74 L/min, 1.0 m, and 1.60 m, respectively. Additionally, verification experimental results demonstrate that the deviation between the predicted comprehensive score and the actual value was less than 6%. This paper can provide a reference for the design and optimization of UAV spraying systems
Influence of Living and Dead Roots of Gansu Poplar on Water Infiltration and Distribution in Soil
During rapid urbanization, it is necessary to increase soil permeability and soil porosity for reducing urban runoff and waterlogging risk. Woody plants are known to increase soil porosity and preferential flow in soil via living roots growth and dead roots decay. However, the primary results of dead woody plant roots on soil porosity and permeability have been discussed based only on the hypotheses or assumptions of different researchers. In this study, living and dead roots (decayed under natural conditions for more than 5 years) of Gansu poplar trees (Populus gansuensis) were selected. They were selected to compare the influence between living and dead roots on water infiltration rate and soil porosity in a cylindrical container (diameter = 20 cm, height = 66 cm) under laboratory conditions. Results indicated that the steady-state water fluxes at the bottom of the containers without roots (control), with living roots, and with dead roots were 54.75 ± 0.80, 61.31 ± 0.61, and 55.97 ± 0.59 cm d−1, respectively. Both living roots and dead roots increased the water infiltration rates in soil and also increased the water storage capacity of soil. The water storage capacities of soil without roots, with living roots, and with dead roots were 0.279, 0.317, and 0.322 cm3 cm−3, respectively. The results from SEM indicated that smaller pores (30–50 μm) were in living roots and larger pores (100–1000 μm) were in dead roots. The soil permeability was increased by living roots possibly due to the larger channels generated on the surface of the roots; however, water absorbed into the dead roots resulted in greater water storage capacity
Factors associated with disclosing men who have sex with men (MSM) sexual behaviors and HIV-positive status: A study based on a social network analysis in Nanjing, China
<div><p>Objective</p><p>We explored the factors associated with disclosure of men who have with sex with men (MSM) behaviors and HIV-positive status among HIV-positive MSM in Nanjing, China.</p><p>Methods</p><p>Social network analysis and epidemiological methods were combined in this pilot study. Information about participants’ (egos’) characteristics and behaviors and their social network members (alters) were collected through interview-administered questionnaires. General estimating equation logistic regression analysis was applied in both univariate and multivariate analysis.</p><p>Results</p><p>Eighty-seven HIV-positive MSM participated. Their mean age was 35.9 ±13.81years. They were more likely to disclose their MSM behavior to their friends [adjust Odds Ratio (AOR) = 6.43, 95% confidence interval (CI):3.08–13.42] or to the social network members who were not heterosexual [AOR = 4.40, 95%CI: 2.17–8.91]. Being participants’ friends [AOR = 5.16, 95%CI: 2.03–13.10] or family members [AOR = 6.22, 95%CI: 2.52–15.33] was significantly associated with HIV-positive status disclosure.</p><p>Conclusion</p><p>HIV-positive MSM tended to disclose their MSM behaviors and HIV positive status to close friends, family members or other individuals who were HIV-positive, engaging in MSM behavior, or both. Consequently, it will be an effective way to implement HIV prevention and intervention strategies in both MSM population and their trusted social networks.</p></div
Farmland Obstacle Detection from the Perspective of UAVs Based on Non-local Deformable DETR
In precision agriculture, unmanned aerial vehicles (UAVs) are playing an increasingly important role in farmland information acquisition and fine management. However, discrete obstacles in the farmland environment, such as trees and power lines, pose serious threats to the flight safety of UAVs. Real-time detection of the attributes of obstacles is urgently needed to ensure their flight safety. In the wake of rapid development of deep learning, object detection algorithms based on convolutional neural networks (CNN) and transformer architectures have achieved remarkable results. Detection Transformer (DETR) and Deformable DETR combine CNN and transformer to achieve end-to-end object detection. The goal of this work is to use Deformable DETR for the task of farmland obstacle detection from the perspective of UAVs. However, limited by local receptive fields and local self-attention mechanisms, Deformable DETR lacks the ability to capture long-range dependencies to some extent. Inspired by non-local neural networks, we introduce the global modeling capability to the front-end ResNet to further improve the overall performance of Deformable DETR. We refer to the improved version as Non-local Deformable DETR. We evaluate the performance of Non-local Deformable DETR for farmland obstacle detection through comparative experiments on our proposed dataset. The results show that, compared with the original Deformable DETR network, the mAP value of the Non-local Deformable DETR is increased from 71.3% to 78.0%. Additionally, Non-local Deformable DETR also presents great performance for detecting small and slender objects. We hope this work can provide a solution to the flight safety problems encountered by UAVs in unstructured farmland environments
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