Many real networks are equipped with short diameters, high clustering, and power-law degree distributions. With preferential attachment and network growth, the model by Barabási and Albert simultaneously reproduces these properties, and geographical versions of growing networks have also been analyzed. However, nongrowing networks with intrinsic vertex weights often explain these features more plausibly, since not all networks are really growing. We propose a geographical nongrowing network model with vertex weights. Edges are assumed to form when a pair of vertices are spatially close and/or have large summed weights. Our model generalizes a variety of models as well as the original nongeographical counterpart, such as the unit disk graph, the Boolean model, and the gravity model, which appear in the contexts of percolation, wire communication, mechanical and solid physics, sociology, economy, and marketing. In appropriate configurations, our model produces small-world networks with power-law degree distributions. We also discuss the relation between geography, power laws in networks, and power laws in general quantities serving as vertex weights.

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Musical performance requires motor skills to coordinate the movements of multiple joints in the hand and arm over a wide range of tempi. However, it is unclear whether the coordination of movement across joints would differ for musicians with different skill levels and how inter-joint coordination would vary in relation to music tempo. The present study addresses these issues by examining the kinematics and muscular activity of the hand and arm movements of professional and amateur pianists who strike two keys alternately with the thumb and little finger at various tempi. The professionals produced a smaller flexion velocity at the thumb and little finger and greater elbow pronation and supination velocity than did the amateurs. The experts also showed smaller extension angles at the metacarpo-phalangeal joint of the index and middle fingers, which were not being used to strike the keys. Furthermore, muscular activity in the extrinsic finger muscles was smaller for the experts than for the amateurs. These findings indicate that pianists with superior skill reduce the finger muscle load during keystrokes by taking advantage of differences in proximal joint motion and hand postural configuration. With an increase in tempo, the experts showed larger and smaller increases in elbow velocity and finger muscle co-activation, respectively, compared to the amateurs, highlighting skill level-dependent differences in movement strategies for tempo adjustment. Finally, when striking as fast as possible, individual differences in the striking tempo among players were explained by their elbow velocities but not by their digit velocities. These findings suggest that pianists who are capable of faster keystrokes benefit more from proximal joint motion than do pianists who are not capable of faster keystrokes. The distinct movement strategy for tempo adjustment in pianists with superior skill would therefore ensure a wider range of musical expression.

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Personal robots, which are expected to become popular in the future, are required to be active in joint work and community life with humans. Such robots must have no bad physical or psychical effect on humans. The psychical effect of a robot on humans has been subjectively measured using questionnaires. However, it has not been objectively measured yet. Human emotion and the consciousness direction can be measured by physiological parameters and body motion, respectively. Therefore, the bioinstrumentation system WB-1 was developed in order to objectively measure the psychical effect of a robot on a human. It can measure physiological parameters such as respiration, heart rate, perspiration and pulse wave, and arm motion. Analyzing human stress in the interaction with a robot from electrocardiogram, the robot could generate a motion for decreasing the stress.