Xin Zhang

With the expansion of the digital business line, the network flow behind the digital power grid is also exploding. To prevent network congestion, this article proposes a novel network flow forecasting model, which is composed of variational mode decomposition, GRU-xgboost block, and a forecasting adjustment block, to grasp the changing patterns and trends of network flow in advance, and to formulate reasonable and effective flow management strategies and meet the requirements of users for network service quality. The network flow series in power grid enterprise always contain complex patterns and outliers, and VMD is applied to adaptively process complex net flow time series into several subseries with simpler patterns. A GRU-xgboost block is designed to reconstruct the features of historical series. Then, xgboost model is applied to generate predictions for all decomposed subsignals. For the final predictions, we design a forecasting adjustment block to further remove the influence of random noise. Finally, the empirical results show the superior performance of the proposed model on network flow forecasting task.

The worldwide spreading pandemic, COVID-19, has caused hostile ageism toward older adults. We adopted a new intervention to reduce such hostile ageism. “Imagine that they were Young” referred to the imagination of what an older adult might look like, think, and behave when they were once young, which was a reversed but refined intervention of the widely-used method of “Imagine that you were old.” In the present study, intergenerational tension was primed, and then 205 younger adults in China aged 18–37 were randomly assigned to 3 different conditions, asking them to imagine once older adults were young, or a future aging self, or read an unrelated essay respectively as experimental manipulations. Then they should distribute medical funds worthy of Chinese 1 million to two patients with COVID-19 of 25 and 85 years old indicating their attitudes toward older adults. Finally, we measured their general attitudes and stereotypes toward older adults. Results verified the effectiveness of both interventions, such that younger adults who took either intervention distributed more medical funds and showed more positive aging attitudes toward older adults than those in the control group. Moreover, “Imagine that they were Young” was tested to be even more effective than “Imagine that you were old.” A series of relative mediation models revealed that the stereotype of warmth mediated the effect for both interventions on decreased hostile ageism behaviors, compared with the control condition. While “Imagine that they were Young” could additionally reduce hostile ageism through a higher level of “including the older adults in their self-group.” This new intervention might be a good alternative to eliminate hostile ageism.

Multi-focus image fusion means fusing a completely clear image with a set of images of the same scene and under the same imaging conditions with different focus points. In order to get a clear image that contains all relevant objects in an area, the multi-focus image fusion algorithm is proposed based on wavelet transform. Firstly, the multi-focus images were decomposed by wavelet transform. Secondly, the wavelet coefficients of the approximant and detail sub-images are fused respectively based on the fusion rule. Finally, the fused image was obtained by using the inverse wavelet transform. Among them, for the low-frequency and high-frequency coefficients, we present a fusion rule based on the weighted ratios and the weighted gradient with the improved edge detection operator. The experimental results illustrate that the proposed algorithm is effective for retaining the detailed images.