大庆市西部地下水库人工回灌方案研究

为了缓解大庆市水资源供需矛盾,提高城市供水安全保障程度,利用业已形成的大庆市西部地下水位降落漏斗,建立地下水库实施水资源人工调蓄。综合分析回灌水水源、回灌水水质、回灌水量、回灌方式等,设计大庆市西部地下水库人工回灌方案,通过数值模拟计算,模拟人工调蓄效果。模拟结果表明,人工回灌可使降落漏斗区地下水位有明显上升,采用压采的同时将注水井布设于龙虎泡管线南侧的注水方案,水位恢复效果最为明显,可使漏斗区地下水位回升11 m左右。     

自压式树状管网的两级优化设计模型与神经优化设计

建立了自压式树状管网两级优化设计模型，并用人工神经网络法实现树状管网非线性规划模型的快速求解。采用的人工神经网络技术的两级优化设计模型在适用范围、求解速度和获得最优解能力上，均优于单一的非线性规划模型和线性规划模型，是实现树状管网全局优化设计的一条新途径。     

人工神经网络在我国水科学中的应用与展望

简要介绍了人工神经网络的发展、结构与特点；从预测预报、评价、水灾害防治、水资源配置与管理决策4方面，综合评述了人工神经网络在我国水科学中的应用，并就人工神经网络今后在我国水科学中的应用与研究做了展望。     

Artificial inoculation and susceptibility of Pinus armandii to Dothistroma septosporum

Dothistroma needle blight (DNB) is a serious needle disease of conifers that primarily affects pine species (Pinus spp.). Dothistroma septosporum is one of the DNB pathogens that has a diverse range of host species excluding Pinus armandii. In 15 inoculated P. armandii seedlings, D. septosporum acervuli were observed in 43 infected needles of ten seedlings with a mean disease severity of 1.11% at 25 weeks after inoculations, demonstrating the potential of D. septosporum to cause symptoms on the needles of P. armandii via artificial inoculation. The disease severity of P. armandii was similar to the positive control, Pinus nigra (median 0.75 for P. armandii to 0.70 for P. nigra), thus, P. armandii acts under artificial conditions as a susceptible host species.     

基于径流分类的日径流量预测神经网络模型

利用聚类分析法将径流序列分解为若干个子径流序列 ,对这些子径流序列分别建立局部神经网络模型 ,而后把这些局部模型合并成一个混合模型。当新的信息进入该模型时 ,首先用分类器判别其类别 ,以确定用混合模型中的何种局部模型加以模拟。通过与不加分类的总体神经网络模型的模拟结果加以对比 ,结果表明这种基于径流分类的降雨 -径流模型表现出了更优良的性能 ,可以较大地提高径流模拟精度。     

铁皮石斛人工繁育技术

以铁皮石斛商品瓶苗和盆栽苗茎段为试材,添加不同浓度6-BA、NAA,诱导茎段腋芽、原球茎和不定根,建立再生体系,快速繁殖铁皮石斛组培苗,以期为秦岭淮河一线以北地区人工繁殖铁皮石斛提供参考依据。结果表明:外植体宜采用70%酒精浸泡20 s,0.1%升汞浸泡8 min消毒;MS+6-BA 3.0~5.0 mg·L^-1+NAA 0.1~1.0 mg·L^-1适合腋芽分化和增殖,分化率92%~100%;1/2MS+6-BA 2.0 mg·L^-1+NAA 0.5 mg·L^-1+土豆泥15%适合原球茎诱导增殖,增殖倍数7.7;1/2MS+NAA 0.3 mg·L^-1生根率为100%;以松针土移栽,成活率83%。     

Neural network prediction of performance parameters of an inclined plate seed metering device and its reverse mapping for the determination of optimum design and operational parameters

Seed planting equipment with inclined plate seed metering devices is the most commonly used equipment for planting of peanut crop in India. For obtaining the high yield, it is very essential to drop the peanut seeds in rows maintaining accurate seed rate and seed spacing with minimum damage to seeds during metering. This mainly depends on forward speed of the planting equipment, rotary speed of the metering plate and area of cells on the plate. The relationship between these factors and the performance parameters viz., seed rate, seed spacing and percent seed damage can be established using regression analysis. But they may not be very accurate and may pose difficulty in the determination of inputs for a set of desired outputs (reverse mapping). Hence, an attempt has been made in this paper to develop the feed forward artificial neural network (ANN) models for the prediction of the performance parameters of an inclined plate seed metering device. The data were generated in the laboratory by conducting experiments on a sticky belt test stand provided with a seed metering device and an opto-electronic seed counter. The generated data was used to develop both statistical and neural network models. The performance of the developed models was compared among themselves for 4 randomly generated test cases. The results show that the ANN model predicted the performance parameters of the seed metering device better than the statistical models. In order to determine the optimum forward speed of the planting equipment, peripheral speed of the metering plate and the area of cells on the plate to obtain the recommended seed rate of 33.33 seeds/m², seed spacing of 100 mm and percent seed damage of 0.2% with 100% fill of the cells, a novel technique of reverse mapping using ANN model was followed. It was observed that the optimum forward speed of the planting equipment and optimum area of cells on the metering plate had good correlation with size of seed. Linear regression equations were developed to predict the optimum forward speed of the planting equipment and optimum area of cells on the metering plate using the size of seeds as independent parameter. The peripheral speed of the metering plate of 0.237 m/s was found to be optimum for the size of seeds in the range of 95.42-123.01 mm². However, the results need to be verified by conducting planting operation under actual field conditions.     

双壳贝类营养需求及人工饵料的研究进展

工厂化养殖是未来贝类产业可持续发展的必然趋势。由于微藻的培养容易受到气温、光照等自然环境条件的影响,而且占用大量水体,因此研发双壳贝类人工饵料已成为贝类工厂化养殖的关键因素。本文简述了蛋白质、脂类和碳水化合物对双壳贝类生长、繁殖及免疫的影响,详细叙述了微藻干粉、大型海藻处理物、酵母、微粒饲料、液态微胶囊饲料和固态微胶囊饲料6种人工饵料的研究进展,以期为研发新型的双壳贝类人工饵料及开展工厂化养殖提供依据。     

人工哺育大熊猫幼仔1月龄内的饲喂及体重增长变化研究

人工圈养大熊猫出生幼仔一般采用自然哺育和人工哺育2种形式。人工哺育大熊猫幼仔1月龄内的饲喂及体重增长变化研究结果表明,3只研究个体均饲喂异母初乳时,其生长发育良好。经方差分析,3只幼仔1月龄内饲喂和体重变化均无显著差异。因圈养大熊猫产双胎的几率较大,所以采取人工哺育可以保证幼仔的成活率。目前人工哺育的方式主要是采集初乳及母乳和配制人工乳来进行人工育幼。