丘陵半干旱区春小麦耗水特征与水分高效利用

该文利用Penman-Monteith公式和田间灌溉试验，对丘陵半干旱区春小麦的需水耗水特征进行了计算分析，并利用旱棚和田间试验、数理统计和系统分析等方法，对冬灌和涌流灌溉的节水效果、喷灌制度和喷灌水量分布、水肥耦合互馈作用、有限水量的最优分配等进行了综合研究.通过模拟寻优得出了春小麦不同产量水平下的水肥管理优化方案.以Jensen模型为基础，利用动态规划确定了不同初始土壤含水量和可利用灌溉水量下的最优分配决策.

~(125)Cs的能级结构

通过116Cd(14N,5n),Ebeam=65MeV的核反应布居了125Cs的高自旋态.利用在束γ谱学实验方法,进行了γ γ符合测量,使已知的125Cs核能级纲图得到了扩展,并且修正了某些组态的带头激发能.

~(126)Cs的高自旋态能级结构

利用融合蒸发反应 116Cd(14 N ,4n) 12 6Cs布居了 12 6Cs的高自旋态 .观测到了 10 0多条新的γ跃迁和相应的能级 ,建立了双奇核 12 6Cs由 9个转动带构成的能级纲图 .尝试性地指定了大部分能级的自旋和宇称以及各转动带的Nilsson单粒子组态 .极大地丰富了已有的实验结果 .

黄土旱区作物-水分模型

本文依据田间试验数据 ,采用Jensen模式 ,研究了黄土旱区冬小麦、春玉米这两种优势作物的—水分模型 .研究结果表明 ,小麦在播种～返青期缺水敏感指数 (λ)最大 ,对缺水最为敏感 ;拔节～抽穗期次之 ,然后是抽穗～灌浆期 ,而灌浆～成熟期和返青～拔节期的敏感性最小 .总耗水量在 32 0～ 42 0mm之间 ,灌水量为 2 6 0～ 30 0mm左右、且分布在冬前和拔节～抽穗期是节水高产高效的灌水模式 .玉米拔节 -抽穗期和抽穗 -灌浆期对缺水最敏感 ,拔节前和灌浆 -成熟期敏感性小 .说明拔节后到抽穗期补水对产量作用最大 ,其次为抽穗 -灌浆期 .这为黄土旱区制定灌溉制度提供了重要理论依据

农业高效用水理论研究综述

农业高效用水包括节水灌溉和旱作农业 ,其核心是提高自然降水和灌溉水的利用效率和效益 .农田蒸散的测定方法各有利弊 .FAO先后建议用Penman修正式和Penman Monteith公式计算参考作物蒸散量 .Jensen乘法模型和Blank加法模型在作物水分生产函数研究中得到广泛应用 .土壤适宜含水量和土壤干旱下限指标的最新研究成果 ,为低定额的农业供水提供了土壤物理学的重要依据 .水分亏缺对与产量形成相关的各个生理过程影响的先后顺序为细胞扩张 >气孔运动 >蒸腾运动 >光合作用 >物质运输 .不很严重的干旱反而对物质运输有促进作用 .农田灌溉研究已由传统的充分灌溉 ,转向非充分灌溉、调亏灌溉和控制性分根交替灌溉 .未来农业高效用水理论将在界面、土壤水动力学、生物节水、缺水逆境等方面深入开展研究 .

Shear induced mixing/demixing: Blends of homopolymers, of homopolymers plus copolymers, and blends in solution

Shear may shift the phase boundary towards the homogeneous state (shear induced mixing, SIM), or in the opposite direction (shear induced demixing, SID). SIM is the typical behavior of mixtures of components of low molar mass and polymer solutions, SID can be observed with solutions of high molar mass polymers and polymer blends at higher shear rates. The typical sequence with increasing shear rate is SIM, then occurrence of an isolated additional immiscible area (SLD), melting of this island into the main miscibility gap, and finally SIM again. A three phase line originates and ends in two critical end points. Raising pressure increases the shear effects. For copolymer containing systems SID is sometimes observed at very low shear rates, preceding the just mentioned sequence of shear influences.

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.

Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches

R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 16(12): 1457-1471. 2004.

The evaluation of evidence for exponentially distributed data

C.G.G. Aitken, Q. Shen, R. Jensen and B. Hayes. The evaluation of evidence for exponentially distributed data. Computational Statistics & Data Analysis, vol. 51, no. 12, pp. 5682-5693, 2007.

Feature Selection based on Rough Sets and Particle Swarm Optimization

X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen. Feature Selection based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters, vol. 28, no. 4, pp. 459-471, 2007.

Rough sets, their extensions and applications

Q. Shen and R. Jensen, 'Rough sets, their extensions and applications,' International Journal of Automation and Computing (IJAC), vol. 4, no. 3, pp. 217-218, 2007.

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

X. Wang, J. Yang, R. Jensen and X. Liu, 'Rough Set Feature Selection and Rule Induction for Prediction of Malignancy Degree in Brain Glioma,' Computer Methods and Programs in Biomedicine, vol. 83, no. 2, pp. 147-156, 2006.

Fuzzy-Rough Data Reduction with Ant Colony Optimization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Data Reduction with Ant Colony Optimization,' Fuzzy Sets and Systems, vol. 149, no. 1, pp. 5-20, 2005.

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.

Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring.

Q. Shen and R. Jensen, 'Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring,' Pattern Recognition, vol. 37, no. 7, pp. 1351-1363, 2004.