无线局域网在解放军医学图书馆的应用     

甄保社  董秋生  罗荣庆  马骏涛 《农业环境科学学报》2007,(4)

 

结合解放军医学图书馆无线局域网络的实际应用情况，从无线局域网的概况、应用特点及安全防护等几方面介绍了无线局域网络的相关知识。

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农电企业配电网GIS系统空间数据库设计   总被引：2，自引：1，他引：1  

房俊龙  张长利  于寿馨 《东北农业大学学报》2006,37(4):508-511

农电企业配电网络数据类型复杂、数据量大,如何合理组织配电系统的数据,为配电GIS系统的高级分析功能或企业层决策提供支持,进而配电网GIS系统设计的关键问题。文章介绍了配电网GIS系统数据组织、数据采集方法及空间数据库构建方法,满足系统需求。  相似文献   

绥化市秦家镇农田防护林网络架构及生态农业效益分析     

李继红 《东北林业大学学报》2006,34(6):101-102,106

对松嫩平原上一典型土地开发整理项目区深入调查研究基础上,在人均土地面积较少的农村,构建出由农田防护林、水利排灌、道路、农田4项网络构成的高效农田防护林复合经营与多样型林带乔、灌立体混交经营模式,实现了林带由单一结构向多层次的立体结构转移,架构了生态立体农业体系,旨为重新调整土地利用结构,实现山、水、田、林、路的全面治理提供依据。  相似文献   

广东省海洋捕捞产量灰色预测   总被引：2，自引：0，他引：2  

陈文河  梁振林 《海洋水产研究》2006,27(5):74-78

为摸清今后海洋捕捞业的发展动态,以广东省海洋与渔业局提供的1996～2003年广东省海洋捕捞业统计数据为基础,分别建立灰色预测模型,通过模型对广东省海洋捕捞业发展趋势做出了近5年预测。预测结果,2010年海洋捕捞产量在167×104t,南海的捕捞产量为156.78×104t,远洋渔业产量为19.82×104t。通过对预测结果的可信度进行分析,进一步确定这种分析结果的合理性,从而为确定今后广东省海洋捕捞业发展方向,渔业生产部门制订投资决策和合理安排生产提供科学依据。  相似文献   

一种改进深度卷积神经网络的海岛识别方法     

王振华  曲念毅  钟元芾  何婉雯  宋巍  黄冬梅 《上海海洋大学学报》2020,29(3):474-480

受不规律潮汐的影响,现有的海岛地物类别自动识别方法存在精度低和时效性差等问题,通过改进深度卷积神经网络提出了一种基于遥感影像的海岛快速识别方法:(1)在深度卷积神经网络的卷积层中增设1×1的卷积核作为瓶颈单元,对多波段的遥感影像进行降维;(2)在池化层引入了重采样方法,基于灰度值对海量的遥感影像进行特征压缩。以300景Landsat-8遥感影像为源数据,分别采用CNN、RCNN和本文改进的深度卷积神经网络对遥感影像中的海岛进行识别,实验结果表明:(1)改进的深度卷积神经网络降低了海岛识别的计算耗时,其计算耗时仅为CNN的4.56%和RCNN的5.60%;(2)改进的深度卷积神经网络较CNN和RCNN提高了海岛识别的精度,识别精度分别为96.0%、93.3%和95.0%。结果说明,改进的深度卷积神经网络适用于面向遥感影像的海岛自动识别。  相似文献   

互联网+背景下智慧农机大数据平台发展策略研究          下载免费PDF全文

周爽爽 《现代农业研究》2020,(4):75-76

随着“互联网＋”时代的到来,数据挖掘、数据共享、云计算等信息技术为农机的智能化和信 息化管理提供有效方法和手段。为更好的推动农业经济发展,有必要借助先进的信息技术手段,搭建 一个基于“互联网＋”的智慧农机管理信息数据共享平台,使我国农机管理进入大数据时代,推进智慧 农业发展。  相似文献   

Marine-Derived Chitosan Nanoparticles Improved the Intestinal Histo-Morphometrical Features in Association with the Health and Immune Response of Grey Mullet (Liza ramada)     

Mahmoud A.O. Dawood  Mahmoud S. Gewaily  Ali A. Soliman  Mustafa Shukry  Asem A. Amer  Elsayed M. Younis  Abdel-Wahab A. Abdel-Warith  Hien Van Doan  Adel H. Saad  Mohamed Aboubakr  Hany M.R. Abdel-Latif  Sabreen E. Fadl 《Marine drugs》2020,18(12)

Marine-derived substances are known for their beneficial influences on aquatic animals’ performances and are recommended to improve intestinal health, immunity, and anti-oxidative status. The present study investigates the role of chitosan nanoparticles on the intestinal histo-morphometrical features in association with the health and immune response of Grey Mullet (Liza ramada). Chitosan nanoparticles are included in the diets at 0, 0.5, 1, and 2 g/kg and introduced to fish in a successive feeding trial for eight weeks. The final body weight (FBW), weight gain (WG), and specific growth rate (SGR) parameters are significantly increased while feed conversion ratio (FCR) decreases by chitosan nanoparticles compared to the control (p < 0.05). The morphometric analysis of the intestines reveals a significant improvement in villus height, villus width, and the number of goblet cells in chitosan-treated groups in a dose-dependent manner. Additionally, there is a positive correlation between the thickness of the enterocyte brush border and the chitosan dose, referring to an increasing absorptive activity. Histologically, the intestinal wall of Grey Mullet consists of four layers; mucosa, sub-mucosa, tunica muscularis (muscular layers), and serosa. The histological examination of the L. ramada intestine shows a normal histo-morphology. The epithelial layer of intestinal mucosa is thrown into elongated finger-like projections, the intestinal villi. The values of hemoglobin, hematocrit, red blood cells (RBCs), total protein (TP), albumin, and globulin are significantly increased in fish fed 1, and 2 g/kg of chitosan nanoparticles compared to fish fed 0 and 0.5 g/kg (p < 0.05). The highest levels of TP and albumin are observed in fish fed 1 g/kg diet (p < 0.05). The lysozyme activity and phagocytic index are significantly enhanced by feeding chitosan nanoparticles at 0.5, 1, and 2 g/kg, whereas the phagocytic activity is improved in fish fed 1 and 2 g/kg (p < 0.05). The highest lysozyme activity and phagocytic index are observed in fish fed 1 g/kg. SOD is significantly activated by feeding chitosan nanoparticles at 1 g/kg. Simultaneously, glutathione peroxidase (GPx) and catalase (CAT) activities also are enhanced by feeding chitosan at 1 and 2 g/kg, compared to fish fed 0 and 0.5 g/kg (p < 0.05). The highest GPx and CAT activities are observed in fish fed 1 g/kg (p < 0.05). Conversely, the malondialdehyde (MDA) levels are decreased by feeding chitosan at 1 and 2 g/kg, with the lowest being in fish fed 1 g/kg (p < 0.05). To summarize, the results elucidate that L. ramada fed dietary chitosan nanoparticles have a marked growth rate, immune response, and anti-oxidative response. These improvements are attributed to the potential role of chitosan nanoparticles in enhancing intestinal histo-morphometry and intestinal health. These results soundly support the possibility of using chitosan nanoparticles at 1–2 g/kg as a feasible functional supplement for aquatic animals.  相似文献   

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index     

Xiuliang Jin  Zhenhai Li  Haikuan Feng  Zhibin Ren  Shaokun Li 《作物学报(英文版)》2020,(1):87-97

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.  相似文献   

基于粒子群算法优化BP神经网络的土壤含水量短期预测模型     

牛曼丽  李新旭  张彦军  雷喜红  王艳芳  李蔚 《蔬菜》2020,(8):24-30

为提高土壤含水量预测精度,基于物联网监测数据,提出了粒子群算法(PSO)优化BP神经网络的土壤含水量预测方法。首先应用主成分分析法筛选出影响土壤含水量的关键影响因子,然后构建8-5-1的BP神经网络拓扑结构,应用粒子群算法优化BP神经网络的初始权值和阈值。结果表明:与传统BP神经网络相比,新模型优化了网络结构,避免了陷入局部最优解,具有良好的预测效果;模型的评价指标平均绝对误差、平均绝对百分误差、误差均方根分别为0.259 2、0.010 5和0.135 6,与单一BP神经网络相比,预测精度更高,可满足实际的土壤含水量预测的需要。  相似文献   

Relevance of the ecological traits of parasitoid wasps and nectariferous plants for conservation biological control: a hybrid meta‐analysis     

Pingyang Zhu  Xusong Zheng  Gang Xie  Guihua Chen  Zhongxian Lu  Geoff Gurr 《Pest management science》2020,76(5):1881-1892

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