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The bridge-type mechanism is one of the most widely used displacement amplifiers in micro-scale applications. However, a bridge-type mechanism with an external load always works in an energy-inefficient situation due to the storage of strain energy in the flexural hinges, which can be validated by the analytical model established in this paper. In fact, in the majority of cases, the energy efficiency of this type of mechanism is only about 50%. To solve this problem, a highly efficient bridge-type mechanism based on negative stiffness is proposed in this paper, which features compact size, simple design, symmetric structure, and also high efficiency. The potential energy in the negative stiffness mechanism acts as an additional energy stream to maintain the total potential energy constant in the system, i.e. the input energy from the actuator can be totally transformed into the output energy, therefore the energy efficiency is close to 100% in an ideal situation. To validate the feasibility of the proposed solution, a prototype of the highly efficient bridge-type mechanism is fabricated. The experimental results show that the efficiency has been improved to 90% approximately when the negative stiffness mechanism is employed. The proposed design can be employed and extended to other compliant mechanisms where high efficiency is required.

Whether in terms of social media platforms, mobile pay apps or an increasing acceptance of RFID chips in humans, technology has transformed everyday life for consumers. Social networks have experienced enormous growth as online personal networking media. Social exchange theory (for motivation and social reward) and theories of collective action can be applied in order to understand how an individual’s behavior may exert effects on or receive influences from other users with regard to the continuance usage intention of social apps. First, this study aims to examine behavioral characteristics of the Millennials, and takes flow and social reward systematically so as to explore SNS users’ continuance based on SNS characteristics. Targeting Millennials SNS users, this study empirically examines users’ continuance intention at individual level and simulates users’ continuance behavior at group level, which are expected to be influential as a next generation of purchasing group, focusing on social network services (SNS) usage. Second, this study tries to suggest strategic implications by identifying key factors that dominate SNS users’ behavior in the process of experiencing SNS. For the empirical purpose, this study analyzes the relationship between SNS characteristics (motivation to use, density, and centrality) and usage behavior (flow, social reward, and continuous intention to use). As a result, each construct of motivation to use SNS, SNS density, and SNS centrality are positively linked with flow. Motivation to use SNS and SNS centrality are positively associated with social reward, however, SNS density does not have a significant effect on social reward. In addition, flow and social reward turn out to have positive linkage with continuous intention to use respectively. The findings of this study are expected to provide implications for researchers and operators in related fields to identify various factors that explain the SNS usages of the Millennials, especially the major factors that sustain SNS involvement and activities. This study can enrich both SNS continuance theory, and help SNS operators to manipulate resources effectively to attract and retain users.

As an effective way to control the network topology, the clustering algorithm can significantly reduce the energy consumption of wireless sensor networks and improve network throughput. Through learning the framework of clustering algorithm for wireless sensor networks, this paper presents a weighted average of cluster head selection algorithm based on BP neural network which make node weights directly related to the decision-making predictions. The weight distribution of nodes is objective. The simulation results show that efficiency of the algorithm in eliminating data redundancy, reducing network traffic, extending the network lifetime.