Water striders are a type of insect with the remarkable ability to stand effortlessly and walk quickly on water. This article reports the water repellency mechanism of water strider legs. Scanning electron microscope (SEM) observations reveal the uniquely hierarchical structure on the legs, consisting of numerous oriented needle-shaped microsetae with elaborate nanogrooves. The maximal supporting force of a single leg against water surprisingly reaches up to 152 dynes, about 15 times the total body weight of this insect. We theoretically demonstrate that the cooperation of nanogroove structures on the oriented microsetae, in conjunction with the wax on the leg, renders such water repellency. This finding might be helpful in the design of innovative miniature aquatic devices and nonwetting materials.

Humans are able to seamlessly integrate tactile and visual stimuli with their intuitions to explore and execute complex manipulation skills. They not only see but also feel their actions. Most current robotic learning methodologies exploit recent progress in computer vision and deep learning to acquire data-hungry pixel-to-action policies. These methodologies do not exploit intuitive latent structure in physics or tactile signatures. Tactile reasoning is omnipresent in the animal kingdom, yet it is underdeveloped in robotic manipulation. Tactile stimuli are only acquired through invasive interaction, and interpretation of the data stream together with visual stimuli is challenging. Here, we propose a methodology to emulate hierarchical reasoning and multisensory fusion in a robot that learns to play Jenga, a complex game that requires physical interaction to be played effectively. The game mechanics were formulated as a generative process using a temporal hierarchical Bayesian model, with representations for both behavioral archetypes and noisy block states. This model captured descriptive latent structures, and the robot learned probabilistic models of these relationships in force and visual domains through a short exploration phase. Once learned, the robot used this representation to infer block behavior patterns and states as it played the game. Using its inferred beliefs, the robot adjusted its behavior with respect to both its current actions and its game strategy, similar to the way humans play the game. We evaluated the performance of the approach against three standard baselines and show its fidelity on a real-world implementation of the game.

In this paper we study the waves generated over the slipline and their interactions with other waves for Mach reflection in steady two-dimensional supersonic flow. We find that a series of expansion and compression waves exist over the slip line, even in the region immediately behind the leading part of the reflected shock wave, previously regarded as a uniform flow. These waves make the leading part of the slipline, previously regarded as straight, deviate nonlinearly towards the reflecting surface. When the transmitted expansion waves from the upper corner first intersect the slipline, an inflexion point is produced. Downstream of this inflexion point, compression waves are produced over the slipline. By considering the interaction between the various expansion or compression waves, we obtain a Mach stem height, the shape and position of the slipline and reflected shock wave, compared well to computational fluid dynamics (CFD) results. We also briefly consider the case with a subsonic portion behind the reflected shock wave. The global flow pattern is obtained through CFD and the starting point of the sonic line is identified through a simple analysis. The sonic line appears to coincide with the first Mach wave from the upper corner expansion fan after transmitted from the reflected shock wave.

A model based on a thermodynamic approach is proposed for predicting the dynamics of communicable epidemics assumed to be governed by controlling efforts of multiple scales so that an entropy is associated with the system. All the epidemic details are factored into a single and time-dependent coefficient, the functional form of this coefficient is found through four constraints, including notably the existence of an inflexion point and a maximum. The model is solved to give a log-normal distribution for the spread rate, for which a Shannon entropy can be defined. The only parameter, that characterizes the width of the distribution function, is uniquely determined through maximizing the rate of entropy production. This entropy-based thermodynamic (EBT) model predicts the number of hospitalized cases with a reasonable accuracy for SARS in the year 2003. This EBT model can be of use for potential epidemics such as avian influenza and H7N9 in China. epidemics, entropy, inflexion point PACS number(s): 02.90.+p, 05.90.+m, 89.90.+n Citation: Wang W B, Wu Z N, Wang C F, et al. Modelling the spreading rate of controlled communicable epidemics through an entropy-based thermodynamic model.