Using a multi-agent simulation approach composing of evacuee, cell, and signal, this study aims to proactively curb the oversaturation spread in evacuation and improve the evacuation efficiency. The specific innovation lies in the en-route path choice model and oversaturation control model. A logit model incorporating fuzzy multi-attribute utility is used to describe the path choice. A three-threshold throttling strategy is proposed to assign signal parameters for oversaturated intersections. A case study demonstrates that the multi-attribute utility performs effectively in the presentation of uncertain route-choice factors under evacuation background. The throttling strategy can decrease the total evacuation time by 7.6% in the supposed scenario.

To solve the problem of the single-phase ground fault and occurrence of electrical fires due to the residual current in substation AC power systems, a residual current intelligent sensing technology is proposed based on ensemble empirical modal decomposition (EEMD), sample entropy (SE) reconstruction, and fire warning technology using a beetle antennae search algorithm. First, through the residual current monitoring device to collect residual current information, EEMD and SE reconstruction for arc-earth fault diagnosis and an analysis of the differences in the current characteristics of each line after reconstruction are used to determine the fault line. Second, residual current, temperature, and operating voltage as input parameters and fire probability are the output parameters. The input–output relationship is established by a back-propagation neural network (BPNN) and optimized by the beetle antennae search (BAS) algorithm to speed up the convergence and improve the prediction accuracy to establish a substation fire warning scheme. Through simulation experiments, this paper proposes the residual current as a monitoring object method can effectively diagnose ground faults and accurately predict the probability of fire occurrence to ensure the safe and stable operation of substations.

The purpose of this investigation was to examine the technical efficiency of mechanical ventilation nonsurgery (DRG 475) among University Hospital Consortium (UHC) hospitals that consists of volunteer, teaching hospitals across the nation. The data for this study was retrieved from the 1997 UHC database that includes charge and discharge information for 69 hospitals. Data on 7961 patients classified with mechanical ventilation were aggregated to the hospital level. We retained data from a total of 62 hospitals, the other seven hospitals had missing data. The research questions were (1) Do UHC hospitals differ significantly in their efficiencies in the treatment of mechanically ventilated patients? (2) What inputs and outputs contribute most to the inefficiencies associated with mechanical ventilation? Of the 62 hospitals analyzed using data envelopment analysis technique, 10 were considered efficient and 52 were inefficient as compared to their benchmark peers. Efficient and inefficient hospitals did significantly differ between the transferred output variable and between the respiratory, laboratory, and radiology input variables. All inputs demonstrated excessive resource utilization among inefficient hospitals as compared to efficient hospitals. A total reduction of about $19 million dollars in ancillary services would need to occur for inefficient hospitals to approach the frontier of efficient hospitals. This study demonstrates that mechanical ventilation is costly, yet the specified ancillary services are capable of being reduced yielding technical efficiency as demonstrated by 10 efficient hospitals.

Based on multi-phase car-following model proposed by Nagatani, the control theory method is used to analyze the stability of the model. The optimal velocity function is improved to have more turning points. The original optimal velocity with one turning point shows two-phase traffic, while the improved model with [Formula: see text] turning points exhibits [Formula: see text] phase traffic. Control signal is added into the model. Numerical simulation is conducted to show the results for the stability of the model with and without control signal.

As the backup power supply of power plants and substations, valve-regulated lead-acid (VRLA) batteries are the last safety guarantee for the safe and reliable operation of power systems, and the batteries’ status of health (SOH) directly affects the stability and safety of power system equipment. In recent years, serious safety accidents have often occurred due to aging and failure of VRLA batteries, so it is urgent to accurately evaluate the health status of batteries. Accurate estimation of battery SOH is conducive to real-time monitoring of single-battery health information, providing a reliable guarantee for fault diagnosis and improving the overall life and economic performance of the battery pack. In this paper, first, the floating charging operation characteristics and aging failure mechanism of a VRLA battery are summarized. Then, the definition and estimation methods of battery SOH are reviewed, including an experimental method, model method, data-driven method and fusion method. The advantages and disadvantages of various methods and their application conditions are analyzed. Finally, for a future big data power system backup power application scenario, the existing problems and development prospects of battery health state estimation are summarized and prospected.