Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed in with normal cars that are actually going somewhere. In order to mitigate the influence of cruising for parking on the normal cars, the park-and-visit cruising tests with GPS and cameras was applied to collect the behavior of the cruisers, and the videotapes of traffic flows were used to measure the volume of cruising cars and the traffic status of normal cars, simultaneously. On this basis, a parking time model based on proportional hazard-based duration model was proposed, and the factors affecting cruise for parking were analyzed, including the volume, search time, speed, acceleration, lane-change frequency, and distracted time of the cruising car. The multiple linear regression model was also established to compare with proportional hazard-based duration model results. The results indicated that between 9 and 56 percent of the traffic was cruising for parking, and the average search time was about 6.03 min. The low-speed, volume, high acceleration frequency, and lane-change times of cruising cars have a negative effect on shortening travel time of the normal traffic flow. Conversely, high-speed of cruising cars has a positive effect on shortening travel time of traffic flow. Moreover, travel time changes in varying degrees due to various factors. Under postulated conditions, the model can be used to estimate the travel time. It is hoped that this study will contribute to improve the planning and management of cruising for parking.

Heat shock protein 90 (HSP90) inhibitors are potential drugs for cancer therapy. The inhibition of HSP90 on cancer cell growth largely through degrading client proteins, like Akt and p53, therefore, triggering cancer cell apoptosis. Here, we show that the HSP90 inhibitor 17-AAG can induce the expression of GRP75, a member of heat shock protein 70 (HSP70) family, which, in turn, attenuates the anti-growth effect of HSP90 inhibition on cancer cells. Additionally, 17-AAG enhanced binding of GRP75 and p53, resulting in the retention of p53 in the cytoplasm. Blocking GRP75 with its inhibitor MKT-077 potentiated the anti-tumor effects of 17-AAG by disrupting the formation of GRP75-p53 complexes, thereby facilitating translocation of p53 into the nuclei and leading to the induction of apoptosis-related genes. Finally, dual inhibition of HSP90 and GRP75 was found to significantly inhibit tumor growth in a liver cancer xenograft model. In conclusion, the GRP75 inhibitor MKT-077 enhances 17-AAG-induced apoptosis in HCCs and increases p53-mediated inhibition of tumor growth in vivo. Dual targeting of GRP75 and HSP90 may be a useful strategy for the treatment of HCCs.

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To identify and quantify the factors that influence the risky riding behaviors of electric bike riders, we designed an e-bike rider behavior questionnaire (ERBQ) and obtained 573 valid samples through tracking surveys and random surveys. An exploratory factor analysis was then conducted to extract four scales: riding confidence, safety attitude, risk perception, and risky riding behavior. Based on the exploratory factor analysis, a structural equation model (SEM) of electric bike riding behaviors was constructed to explore the intrinsic causal relationships among the variables that affect the risky e-bike riding behavior. The results show that the relationship between riding confidence and risky riding behavior is mediated by risk perception and safety attitudes. Safety attitude was found to be significantly associated with risky riding behaviors. Specifically, herd mentality is most closely related to safety attitudes, which means that those engaged in e-bike traffic management and safety education should pay special attention to riders’ psychological management and education. Risk perception has a direct path to risky riding behaviors. Specifically, stochastic evaluation and concern degree are significantly related to e-bike riders’ risk perception. The findings of this study provide an empirical basis for the creation of safety interventions for e-bike riders in China.

With the concept of sharing economic entering into our lives, many parking Apps are designed for connecting the drivers and vacated parking spaces. However, there are not many drivers who use the mobile Apps to reserve and find available parking spaces, which is largely due to the insufficient information provided by the parking App. In order to better explain, predict, and improve drivers’ acceptance of parking App, the conceptual framework based on technology acceptance model was developed to establish the relationships between the drivers’ intention to accept parking App, trust in parking App, perceived usefulness of parking App, and perceived ease of its use. Then structural equation model was established to analyze the relationship between various variables. The results show that the trust in parking App, perceived usefulness, perceived ease of use, and parking App attributes are the main factors that determine the intention to use parking App. Through the test of direct effect, indirect effect, and total effect in the model, it is found that perceived usefulness has the largest total impact on acceptance intention, with a standardized coefficient of 0.984, followed by parking App attribute (0.743), perceived ease of use (0.384), and trust in parking App (0.381).