Genetic diversity in soybean as determined by RAPD and microsatellite analysis

The random amplified polymorphic DNA (RAPD) and microsatellite techniques were used to evaluate the genetic diversity among 18 soybean genotypes selected for a breeding programme to increase the protein content of varieties adapted for central European growing conditions. Out of 33 random primers used in RAPD reactions, only 12 showed polymorphism useful for characterization of these genotypes. In contrast, all 12 microsatellite primer pairs used in this study detected polymorphism with 2–6 alleles per locus. Similarity measures and cluster analysis were made using RAPD and simple sequence repeat (SSR) data, separately and together. The resulting dendrograms were compared with each other and with the available pedigree information as a control. The dendrogram derived from RAPD data showed some divergence from the pedigree information available for the lines. The dendrograms based on SSR data and SSR data combined with RAPD gave very good agreement with pedigree information. It can be concluded that the combined use of a limited number of RAPD and SSR markers is a useful and reliable means of evaluating genetic relationships of genotypes in the absence of pedigree data.     

An Information System for Vehicle Transport Management

This paper proposes a model framework, which describes the function requirements and the common characterstics of vehicle transport management. With the frame, a computer vehicle transportation managerment information system(QCMIS)is in troduced here and it has already been put into practice. The key technical points is also explained in the paper.     

基层农村科技服务者信息行为与需求的分析

为了解中国信息化建设对基层农村科技服务产生了哪些影响及未来农村科技信息服务发展还面临哪些制约因素。本研究面向农村科技服务者开展问卷调查,基于来自中国14个省级地区的调查结果,从网络认知、网络应用、上网目的、上网地点、科技网站访问频次等方面分析了基层农村科技服务者的信息行为特征,并考察了各地区应用互联网开展科技服务的现状,在此基础上从信息内容和传播方式两个层面总结了农村科技服务者对网络科技信息的需求。最后以实证研究结论为依据,从网络科技信息资源建设、传播方式建设、农村信息服务设施建设、农民信息素质提升等方面提出推进基层农村科技服务信息化发展的对策建议。     

扑草净用于仿刺参养殖塘的安全性研究──基于营养盐在生物沉积物-水界面扩散的视角

温带海域普遍养殖仿刺参,仿刺参养殖塘中常有大型藻类爆发,进而影响仿刺参的生长和生存。通常用扑草净清除大型藻类,由于除草剂的生态毒性,可推断扑草净将影响仿刺参生物沉积物中营养盐的再生,本研究则要验证该假说。将仿刺参生物沉积物暴露于不同浓度的扑草净7天,并测定生物沉积物-水界面营养盐的扩散通量。结果表明:在正常使用的去除大型藻类的扑草净剂量之下,营养盐在仿刺参生物沉积物-水界面的扩散通量不受扑草净的影响。按正常剂量使用扑草净并不影响仿刺参生物沉积物中营养盐的再生与积聚,无需对扑草净对营养盐水平的影响而担忧。研究结果将有助于评估除草剂对海水中营养盐循环的影响。     

Enterprise Information Engineering Model and Its Integrated Support Environment

By analyzing the process of enterprise information engineering construction and intra relationship among information system requirement, business process modeling, application system constructing and project management, one of 4 meta model composed of requirement engineering, process engineering, application system engineering and support engineering is proposed for enterprise information engineering. The developments and applications of the business processes and its application systems are integrated into the same development space and application space. Basing on this, a prototype system composed of requirement modeling environment, business process modeling environment, application system modeling environment, and support engineering environment has been designed and built, which is used for supporting enterprise information engineering constructing. It is valued for the enterprise information engineering construction in our country.     

绥化市土地利用变化图谱分析

为了定量了解研究区土地利用类型转换关系及其变化强度与剧烈程度,笔者基于地学信息图谱分析,运用ERDAS8.5和ARC/INFO9.2软件,以马尔柯夫空间概率模型为基础,结合土地利用动态度模型及土地利用综合动态度模型,利用绥化市31年（1980-2010年）土地利用格局4期的空间数据（1980年、1995年、2000年、2010年）合成一系列土地利用格局变化图谱,通过图谱分析来了解绥化市的土地利用格局变化规律。结果表明：耕地大幅度增加,其净增面积346.4 km2,占研究区总面积超过13%,未利用地（滩地和沼泽地）面积急剧减少,净减少面积为440.9 km2,沼泽地完全消失。草地面积略有增加,增加面积12.1 km2,林地面积减少,减少面积为11.4 km2。城市建设用地净增加面积为29.7 km2。在过去的31年间,综合土地利用动态度急剧上升,土地利用格局发生剧烈变化,土地利用类型单一化明显,城市化进程加快,本研究区中部的滩地与沼泽地变化最为剧烈。     

基于信息扩散理论的东北三省农业洪灾风险评估

为了解决农业洪涝灾害风险评估时常会遇到的小样本问题,采用正态信息扩散方法,将单值观测样本点转化为集值样本点,进行了东北三省的区域农业洪涝灾害风险评估,并利用ArcGIS平台制成了该区域的农业洪灾风险图,直观地展示了东北地区农业洪灾风险的空间分布和空间变化趋势。该结果为政府的减灾和应急管理以及保险公司的农业灾害保险方案规划提供借鉴。     

基于GIS的分布式侵蚀产沙模型及其空间尺度转换研究进展

地理信息系统（GIS）与模型的结合是分布式侵蚀产沙模型的发展方向,而模型的空间尺度转换是其研究的重要方面。笔者对国内外基于GIS的分布式侵蚀产沙模型及其空间尺度转换研究进行了全面综述,结合中国的实际研究情况,总结了目前研究存在的主要问题,最后提出了今后一定时期内中国基于GIS的分布式侵蚀产沙模型及其空间尺度转换研究的发展方向。     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

Application of modified Deep Belief Network in Forecasting Cotton Diseases and Insect Pests

[Objective] The occurrence and development of cotton diseases and insect pests are mainly related to environmental information. Because this environmental information is various, complex and unstable, the study on the prediction methods of cotton diseases and insect pests is a certain challenge. This study aims to establish a forecasting model for the timely and accurate prediction of cotton diseases and insect pests. [Method] A forecasting model of cotton diseases and insect pests is proposed based on environmental information and a modified Deep Belief Network (DBN) that is constructed by a three-layer restricted Boltzmann machine (RBM) and a supervised back-propagation (BP) network. In the method, the RBM is used to transform the original environmental information vectors into a new feature space related to the diseases and pests; the BP network is trained to classify and forecast the features generated by the last RBM layer and two rules of dynamic learning and comparison and dispersion are adopted to accelerate the training process of RBM. The proposed model was validated on a dataset of cotton bollworm, aphids, spider, cotton Verticillium wilt, and Fusarium wilt in a recent six-year period. [Result] Compared with the traditional prediction models of cotton diseases and insect pests, the proposed model can deeply explore the extensive correlation between the occurrence of cotton diseases and pests and environmental information. The results show that the proposed model has a higher accuracy compared with the classical predictive models, and the average forecasting accuracy is above 83%. [Conclusion] The proposed method is an effective crop disease and pest forecasting method that can provide a technical support for preventing cotton disease and insect pests.