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The increasing global prevalence of mosquito-borne viruses has emerged as a significant threat to human health and life. Identifying receptors for these viruses is crucial for improving our knowledge of viral pathogenesis and developing effective antiviral strategies. The widespread application of CRISPR-Cas9 screening have led to the discovery of many mosquito-borne virus receptors. The revealed structures of virus-receptor complexes also provide important information for understanding their interaction mechanisms. This review provides a comprehensive summary of both conventional and novel approaches for identifying new viral receptors and the putative entry factors of the most prevalent mosquito-borne viruses within the Flaviviridae, Togaviridae, and Bunyavirales. At the same time, we emphasize the common receptors utilized by these viruses for entry into both vertebrate hosts and mosquito vectors. We discuss promising avenues for developing anti-mosquito-borne viral strategies that target these receptors. Notably, targeting universal receptors of specific mosquito-borne viruses in both vertebrates and mosquitoes offers dual benefits for disease prevention. Additionally, the widespread use of AI-based machine learning and protein structure prediction will accelerate the identification of new viral receptors and provide new avenues for antiviral drug discovery.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle–bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.