| 1 | Optimization of air quantity regulation in mine ventilation networks using the improved differential evolution algorithm and critical path method显示文摘In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method(CPM) based on the multivariable separate solution strategy,was put forward to search for the global optimal solution more efficiently. In each step of evolution,the feasible solutions of air quantity distribution are firstly produced by the improved differential evolution algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM.Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighborhood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve large-scale generalized ventilation networks optimization problem in the future. | Chen Kaiyan Si Junhong Zhou Fubao Zhang Renwei Shao He Zhao Hongmei | 2015 | International Journal of Mining Science and Technology2015，25，1： | 15 |
| 2 | Biomarkers of aging显示文摘Aging biomarkers are a combination of biological parameters to(i)assess age-related changes,(ii)track the physiological aging process,and(iii)predict the transition into a pathological status.Although a broad spectrum of aging biomarkers has been developed,their potential uses and limitations remain poorly characterized.An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research:How old are we?Why do we get old?And how can we age slower?This review aims to address this need.Here,we summarize our current knowledge of biomarkers developed for cellular,organ,and organismal levels of aging,comprising six pillars:physiological characteristics,medical imaging,histological features,cellular alterations,molecular changes,and secretory factors.To fulfill all these requisites,we propose that aging biomarkers should qualify for being specific,systemic,and clinically relevant. | Aging Biomarker Consortium Hainan Bao Jiani Cao Mengting Chen Min Chen Wei Chen Xiao Chen Yanhao Chen Yu Chen Yutian Chen Zhiyang Chen Jagadish K Chhetri Yingjie Ding Junlin Feng Jun Guo Mengmeng Guo Chuting He Yujuan Jia Haiping Jiang Ying Jing Dingfeng Li Jiaming Li Jingyi Li Qinhao Liang Rui Liang Feng Liu Xiaoqian Liu Zuojun Liu Oscar Junhong Luo Jianwei Lv Jingyi Ma Kehang Mao Jiawei Nie Xinhua Qiao Xinpei Sun Xiaoqiang Tang Jianfang Wang Qiaoran Wang Siyuan Wang Xuan Wang Yaning Wang Yuhan Wang Rimo Wu Kai Xia Fu-Hui Xiao Lingyan Xu Yingying Xu Haoteng Yan Liang Yang Ruici Yang Yuanxin Yang Yilin Ying Le Zhang Weiwei Zhang Wenwan Zhang Xing Zhang Zhuo Zhang Min Zhou Rui Zhou Qingchen Zhu Zhengmao Zhu Feng Cao Zhongwei Cao Piu Chan Chang Chen Guobing Chen Hou-Zao Chen Jun Chen Weimin Ci Bi-Sen Ding Qiurong Ding Feng Gao Jing-Dong JHan Kai Huang Zhenyu Ju Qing-Peng Kong Ji Li Jian Li Xin Li Baohua Liu Feng Liu Lin Liu Qiang Liu Qiang Liu Xingguo Liu Yong Liu Xianghang Luo Shuai Ma Xinran Ma Zhiyong Mao Jing Nie Yaojin Peng Jing Qu Jie Ren Ruibao Ren Moshi Song Zhou Songyang Yi Eve Sun Yu Sun Mei Tian Shusen Wang Si Wang Xia Wang Xiaoning Wang Yan-Jiang Wang Yunfang Wang Catherine CL Wong Andy Peng Xiang Yichuan Xiao Zhengwei Xie Daichao Xu Jing Ye Rui Yue Cuntai Zhang Hongbo Zhang Liang Zhang Weiqi Zhang Yong Zhang Yun-Wu Zhang Zhuohua Zhang Tongbiao Zhao Yuzheng Zhao Dahai Zhu Weiguo Zou Gang Pei Guang-Hui Liu | 2023 | Science China(Life Sciences)2023，66，5： | 2 |
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