Background This study aims to examine interocular differences in the choroidal thickness and vascular density of the choriocapillaris in anisometropic myopes and to further explore the relationship between choroidal blood flow and myopia. Methods The sample comprised 44 participants with anisometropic myopia, aged 9 to 18 years, with normal best-corrected visual acuity. All participants underwent a series of examinations, including spherical equivalent refraction (SER) and axial length (AL), measured by a Lenstar optical biometer and optical coherence tomography angiography (OCTA) scanner. OCT measured the choroidal thickness, vascular density, and flow voids of the choriocapillaris, and a customized algorithm was implemented in MATLAB R2017a with the post-correction of AL. The choroidal thickness was measured at the fovea and 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 mm nasally, temporally, inferiorly, and superiorly to the fovea. The vascular density and the flow voids of the choriocapillaris were measured at a 0.6-mm-diameter central circle, and the 0.6–2.5 mm diameter circle in the nasal, temporal, inferior, and superior regions. Repeated-measured ANOVAs were used to analyze the interocular differences. Partial correlations with the K value and age adjustments were used to study the relationships between the choroidal thickness, the choriocapillaris vascular density and flow voids, the SER and AL. Results The choroidal thickness of the more myopic eyes was significantly thinner than less myopic eyes (P ≤ 0.001), and the flow voids in the more myopic eyes were more than less myopic eyes (P = 0.002). There was no significant difference in the vascular density of the choriocapillaris between the more and less myopic eyes (P = 0.525). However, when anisometropia was more than 1.50 D, the vascular density of choriocapillaris in the more myopic eyes was significantly less than the less myopic eyes (P = 0.026). The interocular difference of the choroidal thickness was significantly correlated with the interocular difference in SER and AL in the center, superior, and inferior regions but not in the nasal or temporal regions. The interocular differences of the vascular density and the flow voids of the choriocapillaris were not correlated with the interocular difference of SER and AL. Conclusions The choroidal thickness is thinner in the more myopic eyes. The flow void is increased, and the vascular density of the choriocapillaris is reduced in the more myopic eyes of children with anisometropia exceeding 1.50 D.

The control of thermal transport across solid/liquid interface has attracted great interests for efficient thermal management in the integrated devices. Based on molecular dynamics simulations, we study the effect of interfacial superlattice structure on the Kapitza resistance between graphene/water interface. Compared to the original interface, introducing interfacial superlattice structure can result in an obvious reduction of Kapitza resistance by as large as 40%, exhibiting a decreasing trend of Kapitza resistance with the decrease of superlattice period. Surprisingly, by analyzing the structure of water block and atomic vibration characteristics on both sides of the interface, we find the interfacial superlattice structure has a minor effect on the water structure and overlap in the vibrational spectrum, suggesting that the improved interfacial heat transfer is not mainly originated from the liquid block. Instead, the spectral energy density analysis reveals that phonon scattering rate in the interfacial graphene layer is significantly enhanced after superlattice decoration, giving rise to the increased thermal resistance between the interfacial graphene layer and its nearest neighboring layer. As this thermal resistance is coupled to the Kapitza resistance due to the local nature of interfacial superlattice decoration, the enhanced thermal resistance in the solid segment indirectly reduces the Kapitza resistance between graphene/water interface, which is supported by the enhancement of the spectral interfacial thermal conductance upon superlattce decoration at microscopic level. Our study uncovers the physical mechanism for controlling heat transfer across solid/liquid interface via interfacial superlattice structure, which might provide valuable insights for designing efficient thermal interfaces.

Pedestrian detection and tracking is necessary for autonomous vehicles and traffic management. This paper presents a novel solution to pedestrian detection and tracking for urban scenarios based on Doppler LiDAR that records both the position and velocity of the targets. The workflow consists of two stages. In the detection stage, the input point cloud is first segmented to form clusters, frame by frame. A subsequent multiple pedestrian separation process is introduced to further segment pedestrians close to each other. While a simple speed classifier is capable of extracting most of the moving pedestrians, a supervised machine learning-based classifier is adopted to detect pedestrians with insignificant radial velocity. In the tracking stage, the pedestrian’s state is estimated by a Kalman filter, which uses the speed information to estimate the pedestrian’s dynamics. Based on the similarity between the predicted and detected states of pedestrians, a greedy algorithm is adopted to associate the trajectories with the detection results. The presented detection and tracking methods are tested on two data sets collected in San Francisco, California by a mobile Doppler LiDAR system. The results of the pedestrian detection demonstrate that the proposed two-step classifier can improve the detection performance, particularly for detecting pedestrians far from the sensor. For both data sets, the use of Doppler speed information improves the F1-score and the recall by 15% to 20%. The subsequent tracking from the Kalman filter can achieve 83.9–55.3% for the multiple object tracking accuracy (MOTA), where the contribution of the speed measurements is secondary and insignificant.

Tipping points are sudden, and sometimes irreversible and catastrophic, changes in a system’s dynamical regime. Complex networks are now widely used in the analysis of time series from a complex system. In this paper, we investigate the scope of network methods to indicate tipping points. In particular, we verify that the permutation entropy of transition networks constructed from time series observations of the logistic map can distinguish periodic and chaotic regimes and indicate bifurcations. The permutation entropy of transition networks, the mean edge betweenness of visibility graphs and the number of code words in compression networks, are each shown to indicate the onset of transition of a pitchfork bifurcation system. Our study shows that network methods are effective in detecting transitions. Network-based forecasts can be applied to models of real systems, as we illustrate by considering a lake eutrophication model.