Real-Time Optimization of Pharmacodynamic Target Attainment at Infection Site during Treatment of Post-Neurosurgical Ventriculitis Caused by Carbapenem-Resistant Gram Negatives with Ceftazidime–Avibactam-Based Regimens: A Report of Two Cases

We present two cases of post-neurosurgical ventriculitis caused by carbapenem-resistant Gram-negative pathogens successfully treated with high-dose ceftazidime/avibactam. The existence of a real-time clinical pharmacological advice program, by enabling the optimization of the PK/PD targets over time at the infection site, turned out to be very helpful.

Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain

Real-time energy management strategy (EMS) plays an important role in reducing fuel consumption and maintaining power for the hybrid electric vehicle. However, real-time optimization control is difficult to implement due to the computational load in an instantaneous moment. In this paper, an Approximate equivalent consumption minimization strategy (Approximate-ECMS) is presented for real-time optimization control based on single-shaft parallel hybrid powertrain. The quadratic fitting of the engine fuel consumption rate and the single-axle structure characteristics of the vehicle make the fitness function transformed into a cubic function based on ECMS for solving. The candidate solutions are thus obtained to distribute torque and the optimal distribution is got from the candidate solutions. The results show that the equivalent fuel consumption of Approximate-ECMS was 7.135 L/km by 17.55% improvement compared with Rule-ECMS in the New European Driving Cycle (NEDC). To compensate for the effect of the equivalence factor on fuel consumption, a hybrid dynamic particle swarm optimization-genetic algorithm (DPSO-GA) is used for the optimization of the equivalence factor by 9.9% improvement. The major contribution lies in that the Approximate-ECMS can reduce the computational load for real-time control and prove its effectiveness by comparing different strategies.

Research on the Application of Artificial Intelligence Technology in Power System Intelligent Dispatching Automation

From the perspective of meeting the power quality requirements of users, the article analyses the characteristics of traditional voltage and reactive power control mode and the regional power grid reactive voltage optimization centralized closed-loop control mode (AVC system) based on the dispatch automation system (SCADA/EMS) from the perspective of technical management. Combining the reactive power/voltage real-time optimization control model, a real-time optimization control method of the regional power grid based on the improved differential evolution algorithm is proposed. The particle swarm algorithm is combined with the characteristics of reactive power/voltage control to improve the initial particle quality, reduce the optimization space, and introduce a crossover operator to improve the calculation speed and efficiency of the algorithm. Taking an actual regional power grid as an example, the simulation calculation of reactive power/voltage real-time optimization is carried out. The results show that the proposed algorithm and control strategy are feasible and effective.