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Glaucoma is a group of eye diseases characterized by the thinning of the retinal nerve fiber layer (RNFL), which is primarily caused by the progressive death of retinal ganglion cells (RGCs). Precise monitoring of these changes at a cellular resolution in living eyes is significant for glaucoma research. In this study, we aimed to assess the effectiveness of temporal speckle averaging optical coherence tomography (TSA-OCT) and dynamic OCT (dOCT) in examining the static and potential dynamic properties of RGCs and RNFL in living mouse eyes. We evaluated parameters such as RNFL thickness and possible dynamics, as well as compared the ganglion cell layer (GCL) soma density obtained fromin vivoOCT, fluorescence scanning laser ophthalmoscopy (SLO), andex vivohistology.

 

This research proposes a dynamic resource allocation method for vehicle-to-everything (V2X) communications in the six generation (6G) cellular networks. Cellular V2X (C-V2X) communications empower advanced applications but at the same time bring unprecedented challenges in how to fully utilize the limited physical-layer resources, given the fact that most of the applications require both ultra low latency, high data rate and high reliability. Resource allocation plays a pivotal role to satisfy such requirements as well as guarantee quality of service (QoS). Based on this observation, a novel fuzzy-logic-assisted Q learning model (FAQ) is proposed to intelligently and dynamically allocate resources by taking advantage of the centralized allocation mode. The proposed FAQ model reuses the resources to maximize the network throughput while minimizing the interference caused by concurrent transmissions. The fuzzy-logic module expedites the learning and improves the performance of the Q-learning. A mathematical model is developed to analyze the network throughput considering the interference. To evaluate the performance, a system model for V2X communications is built for urban areas, where various V2X services are deployed in the network. Simulation results show that the proposed FAQ algorithm can significantly outperform deep reinforcement learning, Q learning and other advanced allocation strategies regarding the convergence speed and the network throughput. 

Due to their tunable bandgaps and strong spin-valley locking, transition metal dichalcogenides constitute a unique platform for hosting single-photon emitters. Here, we present a versatile approach for creating bright single-photon emitters in WSe2 monolayers by the deposition of gold nanostars. Our molecular dynamics simulations reveal that the formation of the quantum emitters is caused by the highly localized strain fields created by the sharp tips of the gold nanostars. The surface plasmon modes supported by the gold nanostars can change the local electromagnetic fields in the vicinity of the quantum emitters, leading to their enhanced emission intensities. Moreover, by correlating the emission energies and intensities of the quantum emitters, we are able to associate them with two types of strain fields, and derive the existence of a low-lying dark state in their electronic structures. Our findings are highly relevant for the development and understanding of single-photon emitters in transition metal dichalcogenide materials.