加速广义Laguerre多项式级数的Laplace变换的数值反演方法

本文利用Levin t-变换迭代法，对加速广义Laguerre多项式级数，提出了一种新的Laplace变换的数值反演方法，这种方法在精度上和数值稳定性上的效果都较好。     

两种一年生植物在替代试验中的相互竞争关系

（1）用替代试验研究了燕麦（Aneva sativa）和箭舌豌豆（Vicia sativa）在混播中的竞争相互作用。为了攻混播比例对两种植物在混播条件下的行为影响，田间试验设为5个混播总密度和5种混播比例，共了5次。（2）方差分析表明，相对总生物量（RYT）和竞争平衡指数（CBI）对总密度，混播比例和生长时间的反应敏感。总密度对RYT和CBI的影响高度显著，在大多数情况下，混播比例对RYT和CBI的影响不显著。（3）本文研究的结果表明，持续时间对RYT和CBI很强的影响，在第1次测定时，RYT值接近1而BI值接近0，这一事实表明，种间无竞争或互惠作用。而在生长旺盛期，种间竞争较为剧烈。（4）本研究结果表明，箭舌豌豆的生长严惩受到燕麦的抑制作用，这说明箭舌豌豆在与燕麦混播时表现为竞争上的弱者。     

小麦矮秆育种中性状间关系的多元分析

以7个株高梯度系列的35个系统及5个亲本品种为材料,采用典范分析和逐步回归分析研究了株高及其构成因子与产量因子、产量生理性状间的关系。结果表明:①株高因子通过产量生理性状影响产量因子,一定的生物学产量是获得高产的基础。②矮秆品种的高化对提高产量、千粒重和收获指数有利,对蛋白质含量的影响不明显;高秆品种矮化能显著提高收获指数,对籽粒的蛋白质含量影响不大。③在主要性状符合育种目标的前提下,选择倒二节间较长、第一和第二叶间距略长、穗颈和穗部较长的类型可能对提高产量有利。     

Trends in NE Atlantic landings (southern Portugal): identifying the relative importance of fisheries and environmental variables

Time series of commercial landings from the Algarve (southern Portugal) from 1982 to 1999 were analyzed using min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA). These techniques were used to identify trends and explore the relationships between the response variables (annual landings of 12 species) and explanatory variables [sea surface temperature, rainfall, an upwelling index, Guadiana river (south‐east Portugal) flow, the North Atlantic oscillation, the number of licensed fishing vessels and the number of commercial fishermen]. Landings were more highly correlated with non‐lagged environmental variables and in particular with Guadiana river flow. Both techniques gave coherent results, with the most important trend being a steady decline over time. A DFA model with two explanatory variables (Guadiana river flow and number of fishermen) and three common trends (smoothing functions over time) gave good fits to 10 of the 12 species. Results of other models indicated that river flow is the more important explanatory variable in this model. Changes in the mean flow and discharge regime of the Guadiana river resulting from the construction of the Alqueva dam, completed in 2002, are therefore likely to have a significant and deleterious impact on Algarve fisheries landings.     

植物化感作用影响因素的再认识

土壤养分、酶、微生物、本底特性,根茬还田、水分、植物根系、种植体系和种植模式与化感作用的互作关系,表明这些因子可通过对土壤养分有效性、微生物种群结构、土壤酶活性和化感物质浓度的影响而对化感作用产生直接或间接影响,这些可为通过化感作用途径构建高效农田生态系统提供新的思路.     

Simulation of future dissolved oxygen distribution in pond culture based on sliding window-temporal convolutional network and trend surface analysis

Dissolved oxygen (DO) is a key ecological factor to measure the quality of water in the aquaculture. As the pond water body is affected by the breeding environment, the spatial distribution of DO shows a certain law in the entire pond. Therefore, to simulate the distribution of DO in aquaculture waters and grasp the temporal and spatial variation of DO is the key to achieving precise regulation of DO. For this purpose, this paper proposed a method for simulating the temporal and spatial distribution of DO in pond culture based on a sliding window-temporal convolutional network together with trend surface analysis (SW-TCN-TSA). This paper first utilized SW to construct DO data sets with different prediction durations, and then used the improved TCN model to realize one-dimensional time series prediction for DO at single monitoring point. Based on the prediction results of DO, a TSA method was performed on the predicted values of DO at the extreme moments of all discrete monitoring points, so as to realize the simulation of the temporal and spatial distribution of DO in the pond. Experimental results show that the SW-TCN model has better prediction performance for one-dimensional time series prediction of DO. Compared with traditional deep networks, such as CNN, GRU, LSTM, CNN-GRU and CNN-LSTM, the values of evaluation indicators (MSE, MAE and RMSE) have been greatly improved. In the process of trend surface fitting, all fitting R² of DO at different water depths are higher than 0.9, indicating that the TSA can accurately reflect the temporal and spatial distribution of DO. This method can provide a basis for the prediction and early warning of DO in the three-dimensional space of the pond and has high practicability in aquaculture.     

兴仁小白壳薏苡不同播期对产量的影响

为探索适宜兴仁县薏苡种植的最佳播种时期，在兴仁县开展了薏苡不同播期对产量影响的试验。试验共设9个播期，梯度为7 d，播种时间从3月19日－5月14日。试验结果表明，兴仁小白壳薏苡的最佳播期是3月19日－4月16日，并且在此期间播种，薏苡产量较高。     

吉林莫莫格白鹤春季行为研究

白鹤属大型涉禽,仅分布于亚洲地区,其东亚种群数量为3 750只左右;由于环境变迁,种群数量越来越少,已被列为国家Ⅰ级重点保护动物。为阐明在沼泽湿地生态系统环境中白鹤春季的活动规律以及各种行为的时间分配,于2010年4月15日～5月13日,在吉林莫莫格国家级自然保护区对白鹤春季迁徙期行为进行了初步研究。白鹤的各种行为的时间分配依次为:觅食占64.34%、静栖占4.42%、警戒占13.92%、游走占4.57%、理羽占11.51%、其他(包括打斗、飞行、呜叫等情况)占2.56%。成体与亚成体白鹤、雄鹤与雌鹤在行为和时间分配上有明显差异,在觅食时间上雌鹤高出雄鹤10%左右,雄鹤的警戒时间高于雌鹤。     

基于LS-SVM的小水电站年发电量智能预测模型

针对小水电站年发电量序列的特点,将最小二乘支持向量机(least squares support vector machine,LS-SVM)回归模型引入年发电量预测领域,并给出了相应的过程和算法。与常规基于人工神经网络(artificial neural net-works,ANN)的智能预测方法比较,该模型优点是明显的:①将神经网络迭代学习问题转化为直接求解多元线性方程;②整个训练过程中有且仅有一个全局极值点,确定了预测的稳定性。最后,一个实际的预测例子表明:该模型实现容易、预测准确,适用于小水电站预测。