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The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link The present study explored the bidirectional and reciprocal nature of work-family guilt by testing a non-recursive model that treats work-family guilt as the mediator connecting the work-family interface. The sample was composed of 627 Chinese employees. The findings confirmed the reciprocal nature of work-family guilt (work-to-family and family-to-work guilt), which indicated that employees would not only have one form of guilt in the work-family interface. In addition, the findings revealed that time spent on work/family domains is indirectly related to work-family guilt via the increased work-family conflict and that there was a positive relationship between work-to-family guilt and work performance. As the first study investigating the bidirectional nature of work-family guilt, this study has refined and enriched our understanding of work-family guilt as well as contributed to future work-family interface, emotion, and performance studies.

A semi-empirical model was proposed to estimate the apparent viscosity of gas-fluidized suspensions. This new model was derived by assuming that a suspension consisted of the bubble phase and the emulsion phase. A set of experimental measurements were also conducted to verify the validity of the proposed model as well as the models in the literature. The results indicated that none of the tested literature models provided satisfactory predictions for the fluidized gas-solid suspension over the mentioned target particle volume fraction range. The estimation using the newly proposed model generally well agreed with the experimental apparent viscosity values of the gas-solid suspensions composed of gas-fluidized Geldart Group A or B particles, as most of the estimated values situated within the ±25% error bars. The proposed model is of special significance as it provides a quick estimation method of the apparent viscosity of a gas-solid suspension flow in the engineering calculation.

There are a variety of factors on stage environment such as dramatically changing lights, mutation and similar appearance of performers that make computer vision based performer detection task challenging. Relighting technique is adopted to handle the changing lights problem and can greatly relieve the negative effects caused by changing lights on detection results. Color transfer is one of the commonly used relighting techniques in performer detection task. However, existing color transfer methods will bring artifacts like structural defects, distortions and chromatic noises to processed images that make detection task fail. In this paper, we propose a novel color transfer network to improve the detection results. The proposed network is designed to switch the color distributions between two images. It can either transfers the color style of stage performance to public detection datasets to greatly expand the training datasets, or normalizes the style of captured stage images to some standard template to stable the data distribution. The proposed color transfer network deals with color transfer task in a one-shot framework, i.e. consistently training and predicting, taking advantage of the color name regularization. The effectiveness of the proposed method is validated by experiments on real-captured stage datasets.