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In reality, the quality of an image is generally affected by haze. To obtain a well-quality image, removing haze is a hot issue on theory and application. This paper proposes a new algorithm to remove haze of hazy images. In the algorithm, first, the ambient illumination is estimated by a logarithmic guide filtering that can reserve the characteristics of the bright source areas and improve the dark source areas of the hazy image. Second, to overcome the defect of dark channel prior (DCP) and the over-brightness of the bright channel prior (BCP), two models with two parameters are introduced to improve the DCP and BCP, called multi-channel prior method. At the same time, a self-adaptive method is presented to compute the values of the two parameters. At last, based on the multi-channel prior, a self-adaptive method is proposed to compute the transmission mapping value. Further, four classes hazy images are employed to test the proposed method. The experimental results carried out on the public databases demonstrate that the proposed algorithm can outperform the current state-of-the-arts, including more effective defogging, clearer visibility and richer details.INDEX TERMS Remove haze, hazy image, logarithmic guide filtering, multi-channel prior.

New product development is an important driver of sustainable enterprise development. It is necessary to promote the knowledge sharing of heterogeneous individuals such as design, technology, market, and sociologists. This paper discusses the influence of negative individual knowledge management from the perspective of knowledge-sharing hostility and knowledge manipulation on the performance of new product development. To examine our hypotheses, we conducted a questionnaire survey of 438 employees in China. The results show that although knowledge manipulation contributes to individual innovation performance, it has an inverted U-shaped curve relationship with the team's product development performance. The hostility of knowledge sharing induces knowledge manipulation, which indirectly influences the performance of new product development. The coordination flexibility of R&D teams positively moderates the impact of knowledge manipulation on new product development. Implications and future research directions are discussed.

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In this paper, we state a combining programming approach to optimize traffic signal control problem. The objective of the model is to minimize the total queue length with weight factors at the end of each phase. Then, modified Twin Gaussian Process (MTGP) is employed to predict the arrival rates for the traffic signal control problem. For achieving automatic control of the traffic signal, an intelligent control method of the traffic signal is proposed in view of the combining method, that is to say, the combining method of MTGP and LP, called MTGPLP, is embraced in the intelligent control system. Furthermore, some numerical experiments are proposed to test the validity of the model and the MTGPLP approach. In particular, the results of numerical experiments show that the model is effective with different arrival rates, departure rates, and weight factors and the combining method is successful.