借助Matlab遗传算法反演火山区压力源参数

首先分析了Mogi模型的经典最小二乘反演由于出现矩阵奇异或接近奇异而发散的局限性,决定采用具有全局高效搜索性和优化计算时不需要求导的遗传算法来反演火山区压力源参数。反演程序调用Matlab7.0 Release14工具箱中的选择、交叉和变异等相关程序。最后应用该程序和长白山天池火山区水准和GPS观测资料联合反演火山区压力源参数,经过比较遗传算法和阻尼最小二乘方法的反演结果,可看出2种方法结果近似,可见此方法方便快捷地实现了目标函数的最小化。     

Comparison of Drought-Resistance about Three Wild Rocky-Grasses

In this experiment, using the methods of drought-stress in the pot and PEG simulative drought-stress, three native rocky-slope grasses Pogonatherum panideum（Lam.） Hack, Erioophorum comosum nees and Cynodon dactylon cultivated in the pots were selected as materials to study their drought-resistance by analyzing the indexes such as the leaf area index, the drying roots accumulation, the holding water ability of leaf, the relative conductance of leaf, the soluble protein, chlorophyll. At the same time, by comparing with membership function value of each index, the order of each grass drought-resistance was decided. The results showed that the drought-resistance of Erioophorum comosum nees was stronger than Cynodon dactylon and drought-resistance of Cynodon dactylon was stronger than Pogonatherum panideum （Lam.） Hack. The purpose of the experimental results was to find theoretical foundations for selecting and cultivating native protecting-slope vegetations to adapt to rocky slopes.     

（BN）n及SiB（n-1）Nn（n=2-11）稳定结构的研究

利用遗传算法及Gastreich提出的经验势函数,研究了（BN）n（n=2-11）和SiBn-1Nn（n=2-11）团簇的可能稳定结构,对能量较低的异构体在HF/STO-3G水平进行优化,得到了（BN）n（n=2-11）和SiBn-1Nn（n=2-11）团簇稳定结构单环及线状结构,并进一步分析了单环的结构特征及相对稳定性。     

Effects of self evaluation on late ERP components

High-density event-related potentials (ERPs) were measured when participants were required to evaluate oneself, significant others (father and mother) and the most familiar public figures (Lei Feng and Jiang Qing) based on trait adjectives. There were three referential conditions and a non-referential control condition including self-evaluation (SE), significant other-evaluation (SOE), general other-evaluation (GOE), and semantic positivity-evaluation (SPE). The behavioral data showed that RTs to GOE condition were longer than the other three conditions. Scalp ERP analysis revealed that (1) SPE condition elicited a more negative N2 than SE condition, which was associated with inhibition of responses; (2) SE condition elicited a more positive P300 than SPE condition, which was an index of attention to self-relevant stimuli; (3) there was no difference on the amplitude and latency of P300 between SE and SOE conditions, which reflected that the concept of “self” included intimate others (e.g., father and mother) in Chinese culture.     

遗传算法模式理论研究

给出了多种交叉方式遗传算法的模式定理及相关的证明.该定理避免了遗传算法模式理论的不足,使模式理论更加准确、严格.     

反周期函数(0,P(I))三角插值

通过引进积分算子P(I),研究了反周期函数插值及反周期函数的2-周期(0,P(I))插值,得到了解存在的条件,并给出对应条件下解的显式.     

序列模式挖掘的两种典型算法及比较

序列模式挖掘是数据挖掘的重要分支,GSP算法与PSP算法是序列模式挖掘中的两种典型算法。本文介绍了这两种算法并对其进行了分析与比较。     

A NEW DESCRIPA NEW DESCRIPTOR OF AMINO ACIDS BASED ON THE THREEDIMENSIONAL VECTOR OF ATOMIC INTERACTION FIELD

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) IS A VERY IMPORTANT TOPIC FOR MANY SCIENTIFIC FIELDS AND CAN BE TERMED AS QUANTITATIVE SEQUENCE ACTIVITY MODELS(QSAM)[1] WHEN REFERRED TO THE RESEARCH ON RELATION- SHIPS BETWEEN STRUCTURES AND ACTIVITIES OF BIOLOGICAL MOLECULES, SUCH AS PROTEINS, AND NUCLEIC ACIDS. HOW- EVER, IT IS VERY DIFFICULT TO ESTABLISH A QSAM MODEL,…     

多维函数全局寻优的团队进步算法

通过模仿团队进步需要的学习、探索行为和成员更新规则,提出了一种新颖的双群体演化算法,称为团队进步算法(TPA).算法将一个团队的成员分为精英和普通组,建立了两组的学习样板,定义了学习和探索运算,并合理设定了成员更新规则.两组成员在搜索过程中出现了明显分工,使算法兼备了全局搜索、局部搜索和定向搜索的能力.数值试验结果验证了新算法具有实现简单、全局寻优成功率高、收敛快、计算量少、坚韧性强和参数选择相对容易等特性,对解决优化应用问题具有较大的价值.     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.