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| 1 | Plant drought tolerance trait is the key parameter in improving the modeling of terrestrial transpiration in arid and semi-arid regions显示文摘陆面过程蒸腾作用的模拟制约着天气,气候降水预测的精确度.近几十年来,为了更好地描述植被蒸腾的水力约束,陆面过程模式发展了基于植物性状的植物水力胁迫方案.然而,我们对于植物性状在蒸腾模拟中的地位仍然缺乏了解,植物性状对蒸腾的重要性仍需进一步量化.本研究利用Morris方法评估植物性状参数在通用陆面模式植物水力胁迫方案(CoLM-P_(50)HS)中的重要性,针对17种植物性状,筛选出最为重要的:耐旱性状(P_(50)),气孔性状,和光合作用性状.在12个FLUXNET站点中,参数的重要性由归一化敏感度来衡量.P_(50)的重要性随着降水的减少而增加,而气孔性状和光合作用性状的重要性则随着降水的减少而减少.在干旱或半干旱地区,P_(50)比气孔性状和光合作用性状更重要,这意味着当植物经常经历干旱时,水力安全策略比植物生长策略更关键.而耐旱性状的巨大变异性进一步暗示了多种植物水力安全策略的共存.忽视P_(50)的变异性可能会对陆面过程模式蒸腾作用的模拟造成严重误差.因此,为了更好地表示植物水力功能的变异性,需要增加对耐旱性状的观测并耦合到陆面模式中. | Xintao Liu Xingjie Lu Shulei Zhang Zhongwang Wei Nan Wei Shupeng Zhang Hua Yuan Wei Shangguan Shaofeng Liu Jianfeng Huang Lu Li Xiulan Ye Jinxuan Zhou Wenke Hu Yongjiu Dai | 2022 | Atmospheric and Oceanic Science Letters2022，15，1： | 1 |
| 2 | Monte Carlo Simulation of Fractures Using Isogeometric Boundary Element Methods Based on POD-RBF显示文摘This paper presents a novel framework for stochastic analysis of linear elastic fracture problems.Monte Carlo simulation(MCs)is adopted to address the multi-dimensional uncertainties,whose computation cost is reduced by combination of Proper Orthogonal Decomposition(POD)and the Radial Basis Function(RBF).In order to avoid re-meshing and retain the geometric exactness,isogeometric boundary element method(IGABEM)is employed for simulation,in which the Non-Uniform Rational B-splines(NURBS)are employed for representing the crack surfaces and discretizing dual boundary integral equations.The stress intensity factors(SIFs)are extracted by M integral method.The numerical examples simulate several cracked structures with various uncertain parameters such as load effects,materials,geometric dimensions,and the results are verified by comparison with the analytical solutions. | Haojie Lian Zhongwang Wang Haowen Hu Shengze Li Xuan Peng Leilei Chen | 2021 | Computer Modeling in Engineering & Sciences2021，，7： | 0 |
| 3 | p-Norm Broad Learning for Negative Emotion Classification in Social Networks显示文摘Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks.Most existing methods are based on deep learning models,facing challenges such as complex structures and too many hyperparameters.To meet these challenges,in this paper,we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach(RoBERTa)and p-norm Broad Learning(p-BL).Specifically,there are mainly three contributions in this paper.Firstly,we fine-tune the RoBERTa to adapt it to the task of negative emotion classification.Then,we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors.Secondly,we adopt p-BL to construct a classifier and then predict negative emotions of texts using the classifier.Compared with deep learning models,p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained.Moreover,it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of p.Thirdly,we conduct extensive experiments on the public datasets,and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets. | Guanghao Chen Sancheng Peng Rong Zeng Zhongwang Hu Lihong Cao Yongmei Zhou Zhouhao Ouyang Xiangyu Nie | 2022 | Big Data Mining and Analytics2022，5，3： | 0 |
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