Influence of modified forms of urea and nitrogen levels on weed growth and grain yield of lowland rice

The growth of weeds and their subsequent reduction of rice yield as affected by N source neem cake coated urea (NCU), dicyandiamide coated urea (DCU), rock phosphate coated urea (RPCU), urea supergranules (USG) and prilled urea (PU) was studied on a clay loam soil at Coimbatore, India. Experiments were conducted in northeast monsoon (NEM) 1981, summer 1982, and southwest monsoon (SWM) 1982 seasons.The crop was associated with eleven weed species, and the dominant weeds wereEchinochloa crus-galli, Cyperus difformis andMarsilea quadrifolia. The weed flora varied between seasons. Deep placement of USG reduced the dry weight of weeds in NEM and summer seasons at 60, 90 and 120 Kg N ha^–1 whereas it increased the dry weight at 60 and 90 but not 120 Kg N ha^–1 in SWM season. The dry weight of weeds decreased with increased N rates for all N sources during NEM and summer seasons. In SWM season, dry weight of weeds increased with increased N rates for all N sources except USG. The grain yield of rice was drastically reduced with the deep placement of USG at 60 but not 120 Kg N ha^–1 in SWM season. The differential effect of the N sources between seasons was due to the change of the weed flora. Dominance ofE. crus-galli during SWM season had greater influence on weed dry weight and grain yield of rice.Nitrogen uptake by weeds was frequently greater in unfertilized plots, particularly in NEM and summer seasons. In SWM season, the apparent fertilizer N recovery by weeds was high for USG. It decreased from 53% for 60 Kg USG-N ha^–1 to 4% for 120 Kg USG-N ha^–1.Contribution from the part of Ph.D. work of the first author at Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India.     

自适应收扩系数的双中心协作QPSO算法

针对量子粒子群优化(QPSO)算法迭代后期种群多样性下降、收敛速度慢、易陷入局部最优的缺点,提出一种自适应收缩-扩张系数的双中心协作最子粒子群优化算法。该算法从2个方面进行改进:(1)自适应调节收缩-扩张系数,其目的是帮助粒子跳出局部最优点,提高粒子的全局搜索能力;(2)双重更新全局最优位置,即在每次迭代中,先后分别采用2种不同的方式更新全局最优位置。第1种方式与QPSO算法一致,第2种方式则引入双中心粒子,使其和当前全局最优位置在相应维度上合作,从而达到更新全局最优位置的目的。从固定迭代次数和固定精度角度分析算法性能,仿真结果表明,相比于QPSO算法,该算法在保证复杂度较低的情况下,可提高收敛速度,增强全局和局部搜索能力。     

基于矩阵初等变换的量子可逆逻辑电路双向综合算法

基于矩阵初等变换,提出了量子可逆逻辑电路双向综合算法。该算法依据两数字间的汉明距离,通过交换矩阵行号或矩阵元素对量子可逆逻辑电路的矩阵进行初等行变换。在变换的过程中,利用邻接矩阵的电路转化规则,生成任意给定置换的量子可逆逻辑电路。与其它同类算法相比,由于不需要穷尽搜索,该算法的时空复杂度有大幅降低;又由于采用任意n量子扩展通用Toffoli门,该算法可综合任一置换(奇或偶置换)的量子可逆逻辑电路,并且电路中门的数量有所减少。     

Cycling hardening and serration yielding of Fe-Ni-Cr alloy at high temperature under out-phase TMF

Behaviour of hardening and serration yield of a Fe-Ni-Cr alloy under isothermal cycling (ISC) and out-phase TMF was studied on the basis of varied hysteresis loops. Cycling hardening and serrated yielding for ISC depend on the temperature and the total strain range, stronger hardening with serrated yielding at higher strain range under ISC at 600 °C, but no hardening and serrated yielding occurred under ISC at 800 °C. Stronger hardening with stress serration occurred at the thermal path going to the lowest temperature, no stress serration occurred at the highest temperature under the out-phase. The hardening also depends on the total strain range, higher total strain range with lower cycling temperature resulted in a stronger hardening and remarkable serration yielding behavior. Weaker hardening without serrated yielding occurred at near 800 °C may due to an obvious cycling stress drop under out-phase TMF. Change in the shape of the hysteresis loops also expresses the degree of the damage of the tested alloy under out-phase and ISC.     

负氢离子释放剂对2-甲基-2-丁烯氢转移反应的影响

负氢离子转移反应是氢转移反应的基元反应,促进负氢离子转移反应可以强化选择性氢转移反应。在反应温度510 ℃、剂/油质量比5、质量空速12 h^-1、N₂气氛条件下,分别考察了四氢萘和十氢萘作为负氢离子释放剂对2-甲基-2-丁烯氢转移反应的影响。结果表明：四氢萘和十氢萘均能有效地促进2-甲基-2-丁烯发生选择性氢转移反应,产物中C₅烯烃产率由52.95%最低分别降至37.83%和17.03%,异戊烷产率由17.80%最高分别增加至42.98%和54.58%,焦炭产率由5.28%最低分别降至3.11%和2.85%;且十氢萘比四氢萘具有更好的供氢能力,相同含量的十氢萘对于产物中烯烃产率的降低幅度、异构烷烃产率和选择性的增加幅度的影响均比四氢萘更大,对焦炭产率的抑制作用更强。在含烯烃汽油中分别加入质量分数10%四氢萘和十氢萘后,烯烃产率由11.20%分别降低至5.48%和4.01%;同时能够很好地抑制焦炭的生成,尤其加入十氢萘后焦炭产率由5.30%降到2.82%。     

金属材料屈服强度的应变率效应和热激活理论

对４种金属材料进行了应变率为１０＾－４－１０＾３ｓ＾－１的拉伸试验，结果表明，屈服中度随应变率的升高分两阶段线性升高，提出了位错线热振动对加载频率的响应的热激活作用机理，依据该机理计算所得Ⅰ，Ⅱ区临界应变εｃ与实测值相符，并在此基础上将应变率对掘强度影响的机制图重新划分为：Ｉ区，富热激活区；Ⅱ区，贫热激活区。     

某城区排涝泵站设计流量计算方法初探

采用平均排除法、最大24 h雨型洪水流量时程分配法和产流汇流法3种方法分别计算了某城区电排站的设计流量,并将3种方法的计算结果进行对比分析,选用合适电排站的设计流量,可为类似工程设计流量的确定提供参考。     

Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon–epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.     

一种基于改进的量子神经网络的语音降噪方法

利用一种改进的量子神经网络（IPSO-QNN）在时域上对含噪的语音信号进行降噪处理,文中重点改进了QNN所涉及到的学习算法。针对粒子群算法本身存在早熟的不足,提出了一种改进的粒子群优化算法（IPSO）。该算法通过对早熟粒子的速度和位置叠加随机数据,使其离开局部最优,从而使算法具有更强的寻优能力。利用IPSO对量子神经网络的参数进行训练和学习,建立了比较高效的基于改进的量子神经网的语音信号滤波器,并通过Matlab软件建立实验平台,实验结果表明,新算法充分利用了量子神经计算的快速性以及粒子群算法的全局寻优能力,从而使该语音信号滤波器,具有良好的降噪性能。     

一种量子神经网络模型及改进学习算法

提出一种量子BP网络模型及改进学习算法,该BP网络模型首先基于量子学中一位相移门和两位受控非门的通用性,构造出一种量子神经元,然后由该量子神经元构造隐含层,采用梯度下降法进行学习。输出层采用传统神经元构造,采用基于改进的带动量自适应学习率梯度下降法学习。在UCI两个数据集上采用该模型及算法,实验结果表明该方法比传统的BP网络具有较好的收敛速度和正确率。