Monoterpene concentrations in fresh,senescent, and decaying foliage of singleleaf pinyon (Pinus monophylla Torr. & Frem.: Pinaceae) from the western Great Basin

Senescent foliage from pines is potentially a large contributor to the total monoterpene content of the litter layer, and the availability of these compounds as phytotoxins may result from release of these compounds into the vapor phase. In order to determine the fate of several monoterpene hydrocarbons in the natural environment, we examined their concentrations in fresh, senescent, and decaying needles from 32 single-leaf pinyon pine (Pinus monophylla Torr. & Frem.: Pinaceae) trees growing at two different locations. Total monoterpene content was highest in the fresh needles (mean=5.6 ± 2.2 mg/g extracted air dry weight), but also remained relatively high in senescent needles (mean=3.6 ±1.8 mg/g extracted air dry weight), either still attached to the tree or forming the freshest layer of understory litter. Decaying needles within a dark decomposing layer of litter material 5–20 cm from the surface were found to contain much lower amounts of total monoterpenes (average: =0.12 ±0.06 mg/g extracted air dry weight). Further investigation of the fate of these compounds in the pinyon understory is required to determine if these hydrocarbons are indeed exerting phytotoxic characteristics.     

硼在玉米中的分布特征及化学形态的研究

对施硼肥和不施硼肥的土壤上种植的玉米中硼的分布及化学形态进行了研究 ,结果表明 ,靠近根部的玉米叶子中含硼最多 ,中部和上部叶子中含硼量依次递减 ,玉米种子中的含硼量低于叶子中的含硼量。通过研究不同溶剂对玉米叶子和种子中硼的浸提能力 ,探明了以 10 %醋酸浸提下来的硼最多 ,其次是用 0 .0 1mol/LEDTA浸提下来的硼 ,用水浸提下来的硼的含量最少。     

电子剪切散斑技术在木材无损检测中的应用

复合板材及木制品等表面或内部的缺陷对材料性能有很大影响,如何快速准确地对其进行无损检测,并判断缺陷的位置大小是很有意义的.针对松木板试件,通过预置表面和内部隐藏缺陷来近似模拟实际缺陷情况,提出一种基于剪切散斑干涉技术的木材无损检测技术新方法,实现了对松木试件的无损检测.在表面缺陷的检测中讨论了剪切量对结果的影响,利用像素标定的方法,得到直径为6、5、3mm的缺陷信息;在内部隐藏缺陷检测中,借助热加载技术同时引入相移技术使剪切散斑检测技术不仅具有非接触、全场实时检测等特点,而且完成对内部隐藏近表面缺陷的定量无损检测.整个检测过程光路简单,实时快速,实验结果与实际情况符合较好.     

Improved Computational Model of Grid Cells Based on Column Structure

To simulate the firing pattern of biological grid cells, this paper presents an improved computational model of grid cells based on column structure. In this model, the displacement along different directions is processed by modulus operation, and the obtained remainder is associated with firing rate of grid cell. Compared with the original model, the improved parts include that: the base of modulus operation is changed, and the firing rate in firing field is encoded by Gaussian-like function. Simulation validates that the firing pattern generated by the improved computational model is more consistent with biological characteristic than original model. Besides, the firing pattern is badly influenced by the cumulative positioning error, but the computational model can also generate the regularly hexagonal firing pattern when the real-time positioning results are modified.     

PATTERN RECOGNITION APLLIED TO STABILITY OF FILLED Ti_2Ni PHASES

Pattern recognition method is used for the investigation of stability,region of filled Ti_2Niphases in multi-dimensional bond-parameter space.The filling of C,N and O atoms intoT_6 octahedra consisting of atoms of earhy-transition elements makes the expansion of thestability region of Ti_2Ni phase,and the relative stability of AI_2Cu and MoSi_2 type com-pounds decreases after the introduction of non-metallic elements such as C,N and O.     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

模糊综合评判理论在研究性学习系统中的研究及应用

介绍了工程数学中的模糊综合评判理论以及相应树型向量推理算法,并将模糊综合推理机制运用到研究性学习系统中进行研究型应用。该系统中运用模糊综合评判对学习效果进行合理评判,得出综合成绩,以其作为改进研究性学习模式的参考评证,从而使研究性学习过程良性发展。     

GML模式匹配技术研究

GML是基于XML的空间信息编码标准。GML模式匹配技术作为实现应用模式的集成与共享的关键技术,与GML解析机理和实现技术密切相关。本文详细介绍了关于GML模式匹配的概念;给出了匹配方法的步骤、算法及已所实现的模式匹配详细流程,包括语意与结构相似度判断以及权重的设置;最后,对匹配过程中包含的不确定因素进行了分析与讨论。     

基于MAF模型的串扰时延故障的测试矢量生成

随着深亚微米技术,串扰噪声问题越来越严重。利用MAF模型的基本思想,探讨了一种串扰时延最大化算法,并且利用被修改的FAN算法,生成测试矢量。对于一条敏化通路,利用被修改的FAN算法适当地激活相应的攻击线和受害线,使电路在最恶劣情况下引起最大通路时延,从而实现更有效的时延测试。在标准电路ISCAS’85上进行实验验证,结果表明:该算法对于多攻击线的串扰时延故障的测试矢量产生是有效的。