试论学习型图书馆的创建

阐述了学习型图书馆创建的理论基础，分析了当前国内创建学习型图书馆的障碍所在，并就如何创建学习型图书馆提出了自己的意见。

     

Comparing static versus dynamic protected areas in the Québec boreal forest

Conservation planning is often based on static mapping of species’ ranges or habitat distributions. Succession and disturbance alter, however, habitat quality and quantity through time especially under global climate and land use change scenarios; hence, static protected areas may not ensure habitat persistence and species survival. Here, we examined the relative merits of static and dynamic (floating) protected areas for the conservation of American marten (Martes americana) habitat in a dynamic boreal forest of Québec (Canada). Forest dynamics were modeled using a spatially-explicit landscape disturbance model and protected areas were selected based on the quality and compactness of marten home ranges using MARXAN. Static protected areas were fixed in space during 200 year simulations of boreal forest dynamics, while dynamic protected areas were re-located every 50 years to track dynamic habitat. Dynamic protected areas supported more high quality home ranges through time than static protected areas. The locations of dynamic protected areas were constrained, however, by the highly fragmented forest patterns created through logging and fire in unprotected areas. Our findings emphasize the often-overlooked point that if dynamic conservation planning is to be successful in the long term, the landscape matrix quality surrounding protected areas must be managed in such a way that options remain when it comes to re-planning.     

基于植被指数的三江源高寒草地植物分类与识别方法研究

随着生态健康检测与保护工作的实践以及研究问题的深入,传统的植物分类手段不能完全满足当前研究的需要。因此为研究快速分类识别草地植物的方法,本研究利用ASD (Analytical spectral devices)地物光谱仪,采集了三江源地区高寒草地常见的阿尔泰葶苈(Draba altaica)、高山风毛菊(Saussurea japonica)和车前状垂头菊(Cremanthodium ellisii)等36种植物的原始光谱数据,并选择了比值植被指数等16种高光谱植被指数,基于支持向量机(Support vector machines,SVM)等3种机器学习算法,构建高寒草地植物光谱分类识别模型。研究结果表明：高寒草地植物的原始光谱均符合绿色植物特征,但由于植物形态特征不同光谱差异主要集中在可见光波段;基于植被指数结合3种算法构建的分类模型,精度依次为随机森林(Random forest,RF)(99.4%)>SVM (93.2%)>K邻近算法KNN (88.0%),且模型的预测结果都出现了误判情况;相比SVM与KNN,RF为基于植被指数构建模型的最佳算法,同时能对所构建模型参数进行重要性分析,其中RGI和SAVI为提高RF分类模型精度的两个重要参数。     

现代远程教育中的协作学习设计

远程教育是从管理者都带来了挑战。提出了协作学习设计因“以教师教为中心”到以“学生学为中心”的教学模式的转变,对远程教育的教师、学生和远程教育的特点决定了协作学习方式在远程教育中的重要性。分析了现阶段远程教育的不足,素．为远程教育中教师教学方式和网络平台的设计提供了参考。     

Leaf classification in sunflower crops by computer vision and neural networks

In this article, we present an automatic leaves image classification system for sunflower crops using neural networks, which could be used in selective herbicide applications. The system is comprised of four main stages. First, a segmentation based on rgb color space is performed. Second, many different features are detected and then extracted from the segmented image. Third, the most discriminable set of features are selected. Finally, the Generalized Softmax Perceptron (GSP) neural network architecture is used in conjunction with the recently proposed Posterior Probability Model Selection (PPMS) algorithm for complexity selection in order to select the leaves in an image and then classify them either as sunflower or non-sunflower. The experimental results show that the proposed system achieves a high level of accuracy with only five selected discriminative features obtaining an average Correct Classification Rate of 85% and an area under the receiver operation curve over 90%, for the test set.     

The effect of β-amyloid protein on the behaviors and SOD activity and MDA content of hippocampus in D-galactose-induced aging rats

AIM: To study the effect of β-amyloid protein (β-AP) and D-galactose(D-gal) on learning-memory and SOD activity and MDA content of hippocampus in rats. METHODS: The behaviors of rats were measured by using open field, Y-maze and one-trial passive avoidance response, and the content of SOD and MDA were measured. RESULTS: In the D-gal and D-gal+β-AP group rats, the spontaneous activities and responses to novel environment in the open field were significantly decreased, and the abilities of learning-memory were remarkably attenuated, the content of SOD decreased and MDA content increased markedly in hippocampus (P＜0.05 or P＜0.01 compared with N group). CONCLUSION: The combination of β-AP and D-gal enhanced the damage induced by free radical in hippocampus and decreased the learning-memory ability in rats.     

Effects of rolipram on learning and memory and the activity of PDE4 in hippocampus following the focal brain injury induced by ischemia- reperfusion in rats

AIM: To investigate the effects of rolipram on the ability of learning and memory and the activity of PDE4 in hippocampus following the focal brain injury induced by ischemia- reperfusion in rats. METHODS: The cerebral ischemia-reperfusion injury model was made by middle cerebral artery occlusion (MCAO) in rats. The rats were randomly divided into sham-operated group, model group, and rolipram group. Rolipram was administered once a day (1 mg/kg, ip) from 6 h after the onset of the operation for 2 weeks. Then the learning and memory abilities were tested after Morris water maze and step-though training. The activity of PDE4 in hippocampus was evaluated by HPLC. RESULTS: In the Morris water maze test, compared to sham-operated group, the platform-finding time and swimming distance in model group were significantly longer (P<0.05). Compared to model group, the platform-finding time and swimming distance in rolipram group were significantly shorter (P<0.05). In the step-through test, compared to sham-operated group, the lantent period in model group was significantly shorter (P<0.01) and the error times were statistically increased(P<0.05). Compared with model group, the lantent period in rolipram group were significantly longer (P<0.05), and the error times were markedly decreased. The assay of the HPLC demonstrated that the activities of PDE4 in hippocampus in model group were higher than those in the sham-operated group and rolipram group. CONCLUSION: Rolipram reduces the activity of PDE4 in hippocampus and enhances the ability of learning and memory after the injury induced by ischemia-reperfusion.     

Role of green roofs in urban connectivity,an exploratory approach using landscape graphs in the city of Paris,France

Green roofs provide many ecosystem services, but little is known about the way they contribute to urban functional connectivity. This paper has the following four objectives: (1) to compare the potential green roofs’ role to connectivity in relation to other urban green spaces, (2) to specify the green roofs contribution’s type, (3) to explore the influence of building height integration method and finally (4) to assess the impact on connectivity of simulated greening new roofs. Using a landscape graph approach, we modeled ecological networks of three species groups with different dispersion capacities in the Paris region (France). Then, we computed several connectivity metrics to assess the potential contribution of green roofs to functional connectivity. At a large scale (metropole scale), our results show that green roofs can slightly improve the global connectivity largely through the connections rather than the addition of habitat area. More than a stepping stone function, green roofs would have a dispersion flux function at a local scale. Furthermore, when the difficulty of crossing movement is exponential to the height of buildings, green roofs over 20 m high are mostly disconnected from the ecological networks. In addition to the green roof’s height, our analysis highlights the very strong role played by buildings’ configuration. This study raises promising directions for the integration of building height into the analysis of urban connectivity. Detailed research and long-term biological data from green roofs and green spaces are needed to confirm our results.     

Beyond the least-cost path: evaluating corridor redundancy using a graph-theoretic approach

The impact of the landscape matrix on patterns of animal movement and population dynamics has been widely recognized by ecologists. However, few tools are available to model the matrix’s influence on the length, relative quality, and redundancy of dispersal routes connecting habitat patches. Many GIS software packages can use land use/land cover maps to identify the route of least resistance between two points—the least-cost path. The limitation of this type of analysis is that only a single path is identified, even though alternative paths with comparable costs might exist. In this paper, we implemented two graph theory methods that extend the least-cost path approach: the Conditional Minimum Transit Cost (CMTC) tool and the Multiple Shortest Paths (MSPs) tool. Both methods enable the visualization of multiple dispersal routes that, together, are assumed to form a corridor. We show that corridors containing alternative dispersal routes emerge when favorable habitat is randomly distributed in space. As clusters of favorable habitat start forming, corridors become less redundant and dispersal bottlenecks become visible. Our approach is illustrated using data from a real landscape in the Brazilian Atlantic forest. We explored the effect of small, localized disturbance on dispersal routes linking conservation units. Simulated habitat destruction caused the appearance of alternative dispersal routes, or caused existing corridors to become narrower. These changes were observed even in the absence of significant differences in the length or cost of least-cost paths. Last, we discuss applications to animal movement studies and conservation initiatives.