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| 1 | An M-ALO-SVR Model to Predict Output of Industry-University-Research Collaboration Network in China显示文摘The output prediction of industry-university-research cooperation network is a prerequisite for optimization of network resource allocation and improvement of network innovation performance.Accurate prediction of network output can provide data for feedback systems,offermethods and reference to government macro-level control,and avoid resource wastes caused by improper input of capital and humans.In this paper,a prediction model based on Ant Lion Optimizer and Support Vector Regression is proposed.First,the M-ALO-SVR model was built.Then,Pareto function and regulatory factors were applied to accelerate the convergence of ALO-SVR optimization,improving the global search ability of the algorithm.Finally,the empirical research of the industry-university-research cooperation network was implemented,and simulation experiments were conducted with samples of China Statistical Yearbooks.The results show that the M-ALO-SVR model performs well in the innovation network output prediction.The predictive goodness of fit of the model reaches 99.7%,improved by 0.02%and 3.3%respectively compared with that of ALO-SVR model and SVM.The running time of the model is two seconds fewer than that of the ALO-SVR model.In addition,the optimizing function of the model converges at higher speed and its MSE is optimal. | Ruiqiong Zhong Ben Wang Gege Feng | 2021 | 国际计算机前沿大会会议论文集2021，，2： | 0 |
| 2 | Study on the alternative rate of rural residents' pension and the mechanism of financial allocation显示文摘 | We Gege Feng Shuai | 2015 | International English Education Research2015，，6： | 0 |
| 3 | Integration of urban and rural minimum living standard security system显示文摘 | Feng Shuai Wei Gege | 2015 | International English Education Research2015，，6： | 0 |
| 4 | A novel silicone derivative of natural osalmid(DCZ0858)induces apoptosis and cell cycle arrest in diffuse large B-cell lymphoma via the JAK2/STAT3 pathway显示文摘Diffuse large B-cell lymphoma(DLBCL)is a highly heterogeneous malignant tumor characterized by diffuse growth.DCZ0858 is a novel small molecule with excellent antitumor effects in DLBCL.This study explored in depth the inhibitory effect of DCZ0858 on DLBCL cell lines.Cell Counting Kit-8(CCK-8)and plate colony formation assays were used to evaluate cell proliferation levels.Flow cytometry was employed to analyze apoptosis and the cell cycle,and western blotting was used to quantify the expression of cell cycle regulators.The results indicated that DCZ0858 inhibited cell growth in a concentration-dependent and time-dependent manner while inducing no significant toxicity in normal cells.Moreover,DCZ0858 initiated cell apoptosis via both internal and external apoptotic pathways.DCZ0858 also induced cell cycle arrest in the G0/G1 phase,thereby controlling cell proliferation.Further investigation of the molecular mechanism showed that the JAK2/STAT3 pathway was involved in the DCZ0858-mediated antitumor effects and that JAK2 was the key target for DCZ0858 treatment.Knockdown of JAK2 partly weakened the DCZ0858-mediated antitumor effect in DLBCL cells,while JAK2 overexpression strengthened the effect of DCZ0858 in DLBCL cells.Moreover,a similar antitumor effect was observed for DCZ0858 and the JAK2 inhibitor ruxolitinib,and combining the two could significantly enhance cancer-suppressive signaling.Tumor xenograft models showed that DCZ0858 inhibited tumor growth in vivo and had low toxicity in important organs,findings that were consistent with the in vitro data.In summary,DCZ0858 is a promising drug for the treatment of DLBCL. | Kang Lu Bo Li Hui Zhang Zhijian Xu Dongliang Song Lu Gao Haiguo Sun Liping Li Yingcong Wang Qilin Feng Gege Chen Liangning Hu Rong Wei Yongsheng Xie Dandan Yu Xiaosong Wu Weiliang Zhu Jumei Shi | 2020 | Signal Transduction and Targeted Therapy2020，5，1： | 0 |
| 5 | Two stream skeleton behavior recognition algorithm based on Motif-GCN显示文摘Compared with RGB videos and images,human bone data is less vulnerable to external factors and has stronger robustness.Therefore,behavior recognition methods based on skeletons are widely studied.Because graph convolution network(GCN)can deal with the irregular topology data of hu-man skeletons very well,more and more researchers apply GCN to human behavior recognition.Tra-ditional graph convolution methods only consider the joints with physical connectivity or the same type when building the behavior recognition model based on human skeletons structure,which cannot capture higher-order information better.To solve this problem,Motif-GCN is used in this paper to ex-tract spatial features.The relationship between the joints with natural connection in the human body is encoded by the first Motif-GCN,and the possible relationship between the unconnected joints in the human skeleton is encoded by the second Motif-GCN.In this way,the relationship between non-physical joints can be strengthened.Then a two stream framework combining joint and bone informa-tion is used to capture more action information.Finally,experiments are conducted on two subdata-sets X-Sub and X-View of NTU-RGB+D,and the accuracy shown in Top-1 classification results is 89.5%and 95.4%respectively.The experimental results are 1.0%and 0.3%higher than those of the 2S-AGCN model respectively.The superiority of this method is also proved by the experimental results. | 吴进 WANG Lei FENG Haoran CHONG Gege | 2023 | High Technology Letters2023，29，4： | 0 |
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