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| 1 | Fabrication,properties,and applications of open-cell aluminum foams:A review显示文摘Open-cell metallic foams or porous metals have a distinctive combination of excellent structural performance and superior functional characteristics,such as their light weight,energy absorption,sound absorption,heat dissipation,and electromagnetic shielding.As a primary representative of metallic foams,aluminum foam has developed into a new engineering material with many unique applications in the fields of aerospace,automotive industry,petrochemical industry,building materials,and etc.This paper summarizes the fabrication methods,properties,and applications of open-cell aluminum foams.The current status and development trends are also introduced. | Tan Wan Yuan Liu Canxu Zhou Xiang Chen Yanxiang Li | 2021 | Journal of Materials Science & Technology2021，，3： | 5 |
| 2 | The role of a drug-loaded poly(lactic co-glycolic acid)(PLGA) copolymer stent in the treatment of ovarian cancer显示文摘Objectives:Cisplatin(CDDP)is a widely used and effective basic chemotherapeutic drug for the treatment of a variety of tumors,including ovarian cancer.However,adverse side effects and acquired drug resistance are observed in the clinical application of CDDP.Identifying a mode of administration that can alleviate side effects and reduce drug resistance has become a promising strategy to solve this problem.Methods:In this study,3 D printing technology was used to prepare a CDDP-poly(lactic-co-glycolic acid)(CDDP-PLGA)polymer compound stent,and its physicochemical properties and cytotoxicity were evaluated both in vitro and in vivo.Results:The CDDP-PLGA stent had a significant effect on cell proliferation and apoptosis and clearly decreased the size of subcutaneous tumors in nude mice,whereas the systemic side effects were mild compared with those of intraperitoneal CDDP injection.Compared with the control group,CDDP-PLGA significantly increased the mRNA and protein levels of p-glycoprotein(P<0.01;P<0.01)and decreased vascular endothelial growth factor mRNA(P<0.05)and protein levels(P<0.01),however,CDDP-PLGA significantly decreased the mR NA and protein levels of p-glycoprotein(P<0.01;P<0.01)and vascular endothelial growth factor(P<0.01;P<0.01),which are associated with chemoresistance,in subcutaneous tumor tissue.Immunohistochemistry assay results revealed that,in the CDDP-PLGA group,the staining of the proliferation-related genes Ki67 and PCNA were lightly,and the apoptosis-related gene caspase-3 stained deeply.Conclusions:PLGA biomaterials loaded with CDDP,as compared with the same amount of free CDDP,showed good efficacy in terms of cytotoxicity,as evidenced by changes in apoptosis.Continuous local CDDP release can decrease the systemic side effects of this drug and the occurrence of drug resistance and angiogenesis,and improve the therapeutic effect.This new approach may be an effective strategy for the local treatment of epithelial ovarian cancer. | Yanqing Wang Xiaoyin Qiao Xiao Yang Mengqin Yuan Shu Xian Li Zhang Dongyong Yang Shiyi Liu Fangfang Dai Zhikai Tan Yanxiang Cheng | 2020 | Cancer Biology & Medicine2020，17，1： | 2 |
| 3 | Computing the least-square solutions for centrohermitian matrix problems显示文摘 | Liu Zhongyun Tian Zhaolu Tan Yanxiang | 2006 | Applied Mathematics and Computation2006，174，1： | 1 |
| 4 | The fabrication of reaction-formed silicon carbide with controlled microstructure by infiltrating a pure carbon preform with molten Si显示文摘 | YanXiang Wang ShouHong Tan DongLiang Jiang | 2003 | Ceramics International2003，，3： | 1 |
| 5 | Computing the least - square solutions for eentrohermitian matrix problems 显示文摘 | Liu Zhongyun Tian Zhaolu Tan Yanxiang | 2006 | Applied Mathematics and Computation2006，174，： | 1 |
| 6 | Towards online optimisation of solid oxide fuel cell performance: Combining deep learning with multi-physics simulation显示文摘The use of solid oxide fuel cells(SOFCs)is a promising approach towards achieving sustainable electricity pro-duction from fuel.The utilisation of the hydrocarbons and biomass in SOFCs is particularly attractive owing to their wide distribution,high energy density,and low price.The long-term operation of SOFCs using such fuels remains difficult owing to a lack of an effective diagnosis and optimisation system,which requires not only a precise analysis but also a fast response.In this study,we developed a hybrid model for an on-line analysis of SOFCs at the cell level.The model combines a multi-physics simulation(MPS)and deep learning,overcoming the complexity of MPS for a model-based control system,and reducing the cost of building a database(compared with the experiments)for the training of a deep neural network.The maximum temperature gradient and heat generation are two target parameters for an efficient operation of SOFCs.The results show that a precise predic-tion can be achieved from a trained AI algorithm,in which the relative error between the MPS and AI models is less than 1%.Moreover,an online optimisation is realised using a genetic algorithm,achieving the maximum power density within the limitations of the temperature gradient and operating conditions.This method can also be applied to the prediction and optimisation of other non-liner,dynamic systems. | Haoran Xu Jingbo Ma Peng Tan Bin Chen Zhen Wu Yanxiang Zhang Huizhi Wang Jin Xuan Meng Ni | 2020 | Energy and AI2020，1，1： | 0 |
| 7 | Rating Text Classification with Weighted Negative Supervision on Classifier Layer显示文摘Bidirectional encoder representations from transformers(BERT)gives full play to the advantages of the attention mechanism,improves the performance of sentence representation,and provides a better choice for various natural language understanding(NLU)tasks.Many methods using BERT as the pre-trained model achieve state-of-the-art performance in almost various text classification scenarios.Among them,the multitask learning framework combining the negative supervision and the pre-trained model solves the issue of the model performance degradation that occurs as the semantic similarity of texts conflicts with the classification standards.The current model does not consider the degree of difference between labels,which leads to insufficient difference information learned by the model,and affects classification performance,especially in the rating classification tasks.On the basis of the multi-task learning model,this paper fully considers the degree of difference between labels,which is expressed by using weights to solve the above problems.We supervise negative samples on the classifier layer instead of the encoder layer,so that the classifier layer can also learn the difference information between the labels.Experimental results show that our model can not only performs well in 2-class and multi-class rating text classification tasks,but also performs well in different languages. | ZHANG Jun QIU Longlong SHEN Fanfan HE Yueshun TAN Hai HE Yanxiang | 2023 | Chinese Journal of Electronics2023，32，6： | 0 |
| 8 | Memory Request Priority Based Warp Scheduling for GPUs显示文摘High performance of GPGPU comes from its super massive multithreading, which makes it more and more widely used especially in the field of throughputoriented. Data locality is one of the important factors affecting the performance of GPGPU. Although GPGPU can exploit intra/inter-warp locality by itself in part, there is still large improvement space for that. In our work, we analyze the characteristics of different applications and propose memory request based warp scheduling to better exploit inter-warp spatial locality. This method can make some warps with good inter-warp locality run faster, which is beneficial to improve the whole performance. Our experimental results show that our proposed method can achieve24.7% and 11.9% average performance improvement over LRR and MRPB respectively. | ZHANG Jun HE Yanxiang SHEN Fanfan LI Qing'an TAN Hai | 2018 | Chinese Journal of Electronics2018，27，5： | 0 |
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