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The present study evaluated the protection of Salvia miltiorrhiza injection against acute sodium nitrite poisoning in mice. Forty male C57mice were randomly divided into control group, sodium nitrite poisoning group (SN), Methylene blue (MB) and Salvia miltiorrhizae (SM) group. In SN, MB and SM group, 200 mg/kg sodium nitrite was injected intraperitoneal. Mice in MB and SM group were given 1.5mg/kg Methylene blue and 0.2ml Salvia miltiorrhiza injection immediately after sodium nitrite injection respectively. Mice in SN and SM group demonstrated higher blood methemoglobin and Serum TNFá and IL-6 but lower IL-10 levels than control group (P< 0.05). HE and TUNEL staining showed developed histological damage accompanied with increased apoptosis in heart, lung, liver and kidney. But the values were much less severe in MB and SM group than those in SN group(P< 0.05).The results of this study showed that Salvia miltiorrhizae injection reduced tissue injury induced by sodium nitrite effectively.

Many city residents cannot install their private electric vehicle (EV) charging stations due to lack of dedicated parking spaces or insufficient grid capacity. This presents a significant barrier towards largescale EV adoption. To address this concern, this paper considers a novel business model, on-demand valet charging, that unlocks the potential of under-utilized public charging infrastructure to promise higher EV penetration. In the proposed model, a platform recruits a fleet of couriers that shuttle between customers and public charging stations to provide on-demand valet charging services to EV owners at an affordable price. Couriers are dispatched to pick up out-of-battery EVs from the customer residence, deliver the EVs to the charging station, plug them in, and then return the fully-charged EVs to customers. To depict the proposed business model, we develop a queuing network to represent the stochastic matching dynamics, and further formulate an economic equilibrium model to capture the incentives of couriers, customers as well as the platform. These models are used to examine how charging infrastructure planning and government regulation affect the market outcome. First, we find that the optimal charging station densities for distinct stakeholders are different: couriers prefer a lower density; the platform prefers a higher density; while the density in-between leads to the highest EV penetration as it balances the time traveling to and queuing at charging stations. Second, we evaluate a regulatory policy that imposes a tax on the platform and invests the tax revenue in public charging infrastructure. Numerical results suggest that this regulation can suppress the platform's market power associated with monopoly pricing, increase social welfare, and facilitate market expansion.

This paper studies the optimal spatiotemporal pricing of autonomous mobility-on-demand (AMoD) systems. We consider a platform that operates a fleet of autonomous vehicles (AVs) and determines the pricing, rebalancing, and fleet sizing strategies over the transportation network in response to demand fluctuations. A network flow model is formulated to characterize the evolution of system states over space and time. Fundamental elements in AMoD markets are captured, including demand elasticity, passenger waiting time, vehicle-passenger matching, proactive vehicle rebalancing, and dynamic fleet sizing. The platform's profit maximization problem is cast as a constrained optimal control problem, which is highly nonconvex due to the nonlinear demand model and passenger-vehicle matching model. An integrated decomposition and dynamic programming approach is developed to tackle this optimal control problem, where we first relax the problem through a change of variables, then separate the relaxed problem into a few smallscale subproblems via dual decomposition, and finally obtain the exact solution to each relaxed subproblem through dynamic programming. Despite the non-convexity, the proposed method establishes a theoretical upper bound to evaluate the optimality gap of the obtained solution. The proposed approach is validated with numerical studies using real data from New York City. We find that the platform adopts distinct operation strategies in core and non-core areas of the city because of the asymmetric demand pattern. Furthermore, we also find that low-demand areas are less resilient than high-demand ones when demand surges unexpectedly, because the operator prioritizes supporting high-demand areas at the sacrifice of service quality in lowdemand areas.