During a large daycare center-associated shigellosis outbreak, strains were highly resistant to ampicillin and trimethoprim-sulfamethoxazole. Children were frequently treated with azithromycin and occasionally fluoroquinolones. Appropriate handwashing and diapering infrastructure are necessary to minimize spread of shigellosis within daycare centers, and could reduce use of antimicrobial agents.

This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction.

This paper proposes a method of load ranking and load shedding in a power system based on the calculation of the priority weighting continuity of the power supply of loads and the improved AHP algorithm. The proposed method applies the theories of covariance between objects, correlation, and fuzzy preference to develop a fuzzy preference correlation matrix based on the percentage of Vital Load, Semi Vital Load, and Non-Vital Load at each load bus. This matrix replaces the judgment matrix of the traditional AHP algorithm to form the criteria layers and scheme layers of the problem. The priority weighting continuity of the power supply of loads is continuously calculated and updated according to the load profile and is used to distribute the load shedding power to each load bus. This distribution optimizes the objective function and maximizes the load benefits, thereby minimizing the damages due to load shedding. The traditional AHP method and the proposed method are applied to the IEEE 30 bus system and the result comparison demonstrates the effectiveness of the proposed method.

In a complex system such as the manned Space Station, it is deem necessary that many expert systems must perform tasks in a concurrent and cooperative manner. An important question arise is:what cooperative-task -performing models are appropriate for multiple expert systems to jointly perform tasks. The solution to this question will provide a crucial automation design criteria for the Space Station complex systems architecture. Based on a client /server model for performing tasks, we have developed a system that acts as a front -end to support loosely -coupled communications between expert systems running on multiple Symbolics machines.As an example, we use two ART * -based expert systems to demonstrate the concept of parallel symbolic manipulation for power distribution management and dynamic load planner /scheduler in the simulated Space Station environment. This on -going work will also explore other cooperative-task -performing models as alternatives which can evaluate inter and intra expert system communication mechanisms. It will be served as a testbed and a bench -marking tool for other Space Station expert subsystem communication and information exchange.

This paper presents a load shedding method for power systems with high integration of wind energy, considering their frequency response. The minimum load shedding power needed to restore system frequency to operational limits can be determined by using the modified frequency response model along with secondary frequency control. The voltage electrical distance method can then be applied to appropriately distribute the shedding power to load buses. This method brings selectivity to the problem and minimizes the impact caused by load shedding. The proposed method was validated using simulations on the IEEE 37-bus test system with a modified wind power generator model.