Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.

Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there’s an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.

The past decade has witnessed tremendous progress in soft robotics. Unlike most pneumatic-based methods, we present a new approach to soft robot design based on precharged pneumatics (PCP). We propose a PCP soft bending actuator, which is actuated by precharged air pressure and retracted by inextensible tendons. By pulling or releasing the tendons, the air pressure in the soft actuator is modulated, and hence, its bending angle. The tendons serve in a way similar to pressure-regulating valves that are used in typical pneumatic systems. The linear motion of tendons is transduced into complex motion via the prepressurized bent soft actuator. Furthermore, since a PCP actuator does not need any gas supply, complicated pneumatic control systems used in traditional soft robotics are eliminated. This facilitates the development of compact untethered autonomous soft robots for various applications. Both theoretical modeling and experimental validation have been conducted on a sample PCP soft actuator design. A fully untethered autonomous quadrupedal soft robot and a soft gripper have been developed to demonstrate the superiority of the proposed approach over traditional pneumatic-driven soft robots.

#nofulltext#A new pipe-routing method for aero-engines is proposed in this article. Careful consideration of the spatial characteristics and the primary engineering constraints of aero-engines yielded a new space representation method as well as a space diving method for the aero-engine surface, which simplify the searching space. A genetic algorithm, along with certain modified strategies, including the 'initiation' and 'direction guideline', is also developed for pipe-routing in the sub-spaces. Simulation experiments in the context of various scenes have been carried out to explore the applicability and performance of the propose method. Simulation results showed that this novel method can quickly deliver the optimal routes for any aero-engine of large space and with complex mechanical components while avoiding convergence into local optimal value in comparison with some existing published methods.Published online before print February 1, 2013, 10.1177/095441001247413

In recent years, interest in in-pipe robot research has been steadily increasing. This phenomenon reflects the necessity and urgency of pipe inspection and rehabilitation as several pipe networks have become outdated around the globe. In-pipe robots can be divided into several groups in accordance with their locomotion principles, each with its own advantages and best suited application scope. Research on the screw drive in-pipe robot (SDIR) has had a rising trend due to the robot's simple driving mechanism design and numerous advantages. This study compares and analyzes the characteristics of various SDIRs from the aspects of mechanism design, driving principle, and motion and mechanical behaviors. Each SDIR has its own advantages and disadvantages depending on its design requirements and intended applications. A number of prototypes have been fabricated to verify their functionality and efficiency in inspection tasks. This study can provide an up-to-date reference for researchers to conduct further analysis on SDIRs.

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SUMMARYPipeline grids of various size and material are pervasive in today's modern society. The frequent inspection and maintenance of such pipeline grids have presented a tremendous challenge. It is advocated that only advanced robot design embedded with intelligent electronics and control algorithms could perform the job. Given the ever increasing demands for intelligent in-pipe robots, various in-pipe drive mechanisms have been reported. One of the simplest is helical wheel drives that have only one degree of freedom. All previously reported in-pipe helical drives are based on independent passive wheels that are tilted an angle. One of the major problems of current helical wheel drives is their unstable traction force. In this paper, instead of allowing the wheels to rotate independently, they are synchronized by adding a timing belt. This small change will result in significant improvement which will be highlighted in this paper. In the proposed driving method, tracking force is analyzed together with a comprehensive set of traction force measurement experiments. Both analysis and experiments have shown that the proposed mechanism has great potential for in-pipe robot drive design.

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.