River ice monitoring is of great significance for river management, ship navigation and ice hazard forecasting in cold-regions. Accurate ice segmentation is one most important pieces of technology in ice monitoring research. It can provide the prerequisite information for the calculation of ice cover density, drift ice speed, ice cover distribution, change detection and so on. Unmanned aerial vehicle (UAV) aerial photography has the advantages of higher spatial and temporal resolution. As UAV technology has become more popular and cheaper, it has been widely used in ice monitoring. So, we focused on river ice segmentation based on UAV remote sensing images. In this study, the NWPU_YRCC dataset was built for river ice segmentation, in which all images were captured by different UAVs in the region of the Yellow River, the most difficult river to manage in the world. To the best of our knowledge, this is the first public UAV image dataset for river ice segmentation. Meanwhile, a semantic segmentation deep convolution neural network by fusing positional and channel-wise attentive features is proposed for river ice semantic segmentation, named ICENET. Experiments demonstrated that the proposed ICENET outperforms the state-of-the-art methods, achieving a superior result on the NWPU_YRCC dataset.

D-met administration in advance of noise-exposure, without further administration, significantly protects from noise-induced ABR threshold shift and OHC loss.

A facile controlled synthesis method of gold nanoparticles (AuNPs) under mild conditions has been developed by combining the stabilization ability of amphiphilic thiacalixarene and the reduction ability of phenolic moieties together, and the particle sizes of AuNPs can be readily controlled by only adjusting the feeding ratio of Au/S.

PurposeIn heterogeneously segmented markets, collaborating with product users in product innovation is important for business success. End user innovators and embedded user innovators differ in terms of their prior embeddedness in the target industry. The purpose of this study is twofold. First, the authors empirically compare these two types of user innovators in terms of their diffusion channel selection. Second, the authors analyze how the technological advances of their innovations affect this difference.Design/methodology/approachUsing an online questionnaire survey, this study collected a sample of 237 user-generated innovations in Japan and analyzed several hypotheses using quantitative statistical approaches.FindingsThe analysis shows that embedded user innovators are more likely than end user innovators to transfer their innovations to producers rather than peers. As the technological advances of their innovations increase, end user innovators' likelihood of transferring their innovation to producers increases more significantly than that of embedded user innovators.Originality/valueThis is the first paper to investigate the difference between end user innovators and embedded user innovators with respect to their diffusion channel selection as well as the moderating role of technological advances. The findings bring new perspectives to the domains of user–producer collaboration and technology transfer.

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