In this article, the distributed H' composite-rotating consensus problem is concerned for a class of second-order multiagent systems. First, based on local state feedback and communication feedback, a distributed control algorithm is proposed. Then, sufficient conditions are derived in order to make all agents reach a composite-rotating consensus with the desired H' performance. Finally, the simulations are given to show the effectiveness of the theoretical results.

Abstract. The catastrophic earthquake in 2008 has caused serious damage to Wenchuan County and the surrounding area in China. In recent years, great attention has been paid to the resilience of the affected area. This study applied a new framework, the Resilience Inference Measurement (RIM) model, to quantify and validate the community resilience of 105 counties in the affected area. The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure, damage, and recovery conditions, and then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county resilience. The analysis results show that counties located right at the epicenter had the lowest resilience, but counties immediately adjacent to the epicenter had the highest resilience capacities. Counties that were farther away from the epicenter returned to normal resiliency. The socioeconomic variables, including sex ratio, per capita GDP, percent of ethnic minority, and medical facilities, were identified as the most influential socio-economic characteristics on resilience. This study provides useful information to improve county resilience to earthquakes and support decision-making for sustainable development.

In a sudden natural disaster, a large number of people

Editorial on the Research Topic Perception recovery and augmentation in medical roboticsArtificial intelligence technology has been applied to complex problems in many fields. From the perspective of neuroscience, the continuous development of neuroscience can explain the deep mechanism of human brain work, thus helping researchers develop more powerful artificial intelligence algorithms. From the perspective of brain computer interface, in-depth research in this field can generate more powerful intelligence through the interaction between human brain and machine. Empowered by the recent advances in artificial intelligence and robot technology, medical robotics have found wider and deeper applications in the health care practice. Except helping the doctors in diagnosis and medical treatment, medical robotic systems also contribute to the development of medical informatization while effectively alleviating the shortage of medical resources. On the other hand, it is noticed that, with the use of computer technology and novel robotic tools, doctors may lose some of their intuitive human perception during the robot-aided diagnoses/operations. In this case, remedial and even better solutions have been proposed to address this issue in medical robotics. Examples include high definition endoscope to provide the surgeon with in vivo visual perception of wider working environment, force and tactile feedback generated by the joystick of the console to provide the surgeon with accurate and scaled tactile perception, etc. These solutions can replace and even augment some human perception capabilities with machine perception. It is believed that the advanced machine perception will greatly contribute to the benefits of both the doctors and patients and therefore to the overall progress of medical robotics.After a careful peer-review process, this editorial presents the manuscripts selected for publication in the Research Topic "Perception recovery and augmentation in medical robotics" of Frontiers in Neurorobotics, which includes seven articles. These articles are original research papers covering multiple aspects of medical robotics that include individualized gait guidance in rehabilitation walking training, cone-beam computerized tomography, endoscopic imaging, robust classification of natural hand