Previous research on the factors affecting knowledge sharing has focused on the relationships between a limited number of variables. However, it is unclear how these factors interact with each other and jointly influence knowledge-sharing intentions. Drawing on social cognitive theory (SCT), this paper performs a decision tree analysis to predict the knowledge-sharing intentions of social question-and-answer (Q&A) community members based on a multitude of environmental and individual factors, including a sharing culture, motivations, and individual characteristics. Data from 1,007 users were collected, and a regression tree model was built using the R package rpart. The results show that high levels of knowledge-sharing intentions occur among those who strongly enjoyed sharing and who perceived fairness within the community. For those who had a moderate or low level of enjoyment, their willingness to share knowledge was jointly affected by the sharing culture and extrinsic motivations.

As the feature size of the semiconductor process is scaling down to 10nm and below, it is possible to assemble systems with high performance processors that can theoretically provide computational power of up to tens of PLOPS. However, the power consumption of these systems is also rocketing up to tens of millions watts, and the actual performance is only around 60% of the theoretical performance. Today, power efficiency and sustained performance have become the main foci of processor designers. Traditional computing architecture such as superscalar and GPGPU are proven to be power inefficient, and there is a big gap between the actual and peak performance. In this paper, we present the MaPU architecture, a novel architecture which is suitable for data-intensive computing with great power efficiency and sustained computation throughput. To achieve this goal, MaPU attempts to optimize the application from a system perspective, including the hardware, algorithm and corresponding program model. It uses an innovative multi-granularity parallel memory system with intrinsic shuffle ability, cascading pipelines with wide SIMD data paths and a state-machine-based program model. When executing typical signal processing algorithms, a single MaPU core implemented with a 40nm process exhibits a sustained performance of 134 GLOPS while consuming only 2.8 W in power, which increases the actual power efficiency by an order of magnitude comparable with the traditional CPU and GPGPU.