This paper describes a novel strategy to weaken the piezopotential screening effect by forming Schottky junctions on the ZnO surface through the introduction of Au particles onto the surface. With this approach, the piezoelectric-energyconversion performance was greatly enhanced. The output voltage and current density of the Au@ZnO nanoarray-based piezoelectric nanogenerator reached 2 V and 1 μA/cm2, respectively, 10 times higher than the output of pristine ZnO nanoarray-based piezoelectric nanogenerators. We attribute this enhancement to dramatic suppression of the screening effect due to the decreased carrier concentration, as determined by scanning Kelvin probe microscope measurements and impedance analysis. The lowered capacitance of the Au@ZnO nanoarraybased piezoelectric nanogenerator also contributes to the improved output. This work provides a novel method to enhance the performance of piezoelectric nanogenerators and possibly extends to piezotronics and piezophototronics.
There have been many studies focusing on individuals’ knowledge sharing behavior in the organizational setting. With the rapid prevalence of social networking sites, many people began to express their thoughts or share their knowledge via Facebook website. Facebook is an open environment which does not provide any immediate monetary benefits to its users. Its Groups members’ knowledge sharing behavior could be different from the ones in organizations. We proposed a research model to examine factors which promote the Facebook Groups users’ willingness to share knowledge. The factors in the study include extrinsic motivation, social and psychological forces, and social networking sharing culture. We used PLS to test our proposed hypotheses based on 271 responses collected through an online survey. Our results indicated that reputation would affect knowledge sharing attitude of Groups members and sense of self-worth would directly and indirectly (through subjective norm) affect the attitude. In addition, social networking sharing culture (fairness, identification, and openness) is the most significant factor, not only directly affecting knowledge sharing intention, but also indirectly influencing the sharing intention through subjective norm and knowledge sharing attitude. 相似文献
Language understanding is one of the most important characteristics for human beings. As a pervasive phenomenon in natural language, metaphor is not only an essential thinking approach, but also an ingredient in human conceptual system. Many of our ways of thinking and experiences are virtually represented metaphorically. With the development of the cognitive research on metaphor, it is urgent to formulate a computational model for metaphor understanding based on the cognitive mechanism, especially with the view to promoting natural language understanding. Many works have been done in pragmatics and cognitive linguistics, especially the discussions on metaphor understanding process in pragmatics and metaphor mapping representation in cognitive linguistics. In this paper, a theoretical framework for metaphor understanding based on the embodied mechanism of concept inquiry is proposed. Based on this framework, ontology is introduced as the knowledge representation method in metaphor understanding, and metaphor mapping is formulated as ontology mapping. In line with the conceptual blending theory, a revised conceptual blending framework is presented by adding a lexical ontology and context as the fifth mental space, and a metaphor mapping algorithm is proposed. 相似文献
Network forensics supports capabilities such as attacker identification and attack reconstruction, which complement the traditional intrusion detection and perimeter defense techniques in building a robust security mechanism. Attacker identification pinpoints attack origin to deter future attackers, while attack reconstruction reveals attack causality and network vulnerabilities. In this paper, we discuss the problem and feasibility of back tracking the origin of a self-propagating stealth attack when given a network traffic trace for a sufficiently long period of time. We propose a network forensics mechanism that is scalable in computation time and space while maintaining high accuracy in the identification of the attack origin. We further develop a data reduction method to filter out attack-irrelevant data and only retain evidence relevant to potential attacks for a post-mortem investigation. Using real-world trace driven experiments, we evaluate the performance of the proposed mechanism and show that we can trim down up to 97% of attack-irrelevant network traffic and successfully identify attack origin. 相似文献
