During the 2010 rainy season in Yangbajing (4300 m above sea level) in Tibet, China, a long-duration count enhancement associated with thunderclouds was detected by a solar-neutron telescope and neutron monitors installed at the Yangbajing Comic Ray Observatory. The event, lasting for $40 min , was observed on July 22, 2010. The solar-neutron telescope detected significant -ray signals with energies >40 MeV in the event. Such a prolonged high-energy event has never been observed in association with thunderclouds, clearly suggesting that electron acceleration lasts for 40 min in thunderclouds. In addition, Monte Carlo simulations showed that >10 MeV rays largely contribute to the neutron monitor signals, while >1 keV neutrons produced via a photonuclear reaction contribute relatively less to the signals. This result suggests that enhancements of neutron monitors during thunderstorms are not necessarily clear evidence for neutron production, as previously thought.

Abstract-This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional "trajectory space" of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.

Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people (n = 12) and post-stroke patients (n = 33), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that poststroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for poststroke patients that focuses on activation timing of muscle synergies.

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Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however, remains a long-standing challenge. One effective method of choosing muscle combinations is to create a set containing the muscle combinations of only efficient behaviors, and then to choose combinations from that set. The notion of muscle synergy, which was introduced to divide muscle activations into a lower-dimensional synergy space and time-dependent variables, is a suitable tool relevant to the discussion of this issue. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to control behaviors. In this study, we investigated the mechanism the CNS may use to define the appropriate region and size of the synergy space when performing skilled behavior. Two indices were introduced in this study, one is the synergy stability index (SSI) that indicates the region of the synergy space, the other is the synergy coordination index (SCI) that indicates the size of the synergy space. The results on automatic posture response experiments show that SSI and SCI are positively correlated with the balance skill of the participants, and they are tunable by behavior training. These results suggest that the CNS has the ability to create optimal sets of efficient behaviors by optimizing the size of the synergy space at the appropriate region through interacting with the environment.