一例异育银鲫(Carassius auratus gibelio)暴发性出血病病原分析     

袁雪梅  潘晓艺  郝贵杰  刘莉  吕孙建  蔺凌云  沈锦玉 《海洋与湖沼》2019,50(4):913-920

2016年6月,江苏某异育银鲫(Carassius auratus gibelio)养殖场暴发一种传染性急性出血病,造成养殖银鲫大量死亡。为分析此次疾病病因及流行规律,本研究从发病养殖场采集患出血病的异育银鲫,从细菌、病毒及寄生虫三个方面对病原进行了分析。采用病原菌分离、组织病理学观察、超薄切片电镜观察、病毒核酸分析、回感实验等对病原进行鉴定。结果显示从发病鲫鱼体内分离到病毒一株,未发现寄生虫及细菌感染。经测序及序列分析,该病毒为鲤疱疹病毒Ⅱ型(Cyprinid herpesvirus2,CyHV-2)病毒,组织病理学观察结果显示患病鱼的鳃和肾脏有明显病变,电镜下可观察到病鱼脾脏组织有带囊膜的球形病毒,囊膜直径约为170—200nm,病毒衣壳直径约为110—120nm,核心直径约为60nm,用组织匀浆感染鲫鱼囊胚细胞系(CGB)可稳定地观察到典型的细胞病变,用患病鱼组织匀浆液人工感染异育银鲫的死亡率高达100%,荧光定量PCR检测到该病毒可感染多器官,其中以脾脏中病毒含量最高,其次是脑,肝脏中最少。本研究可为CyHV-2的诊断防控及疫苗研制提供资料。  相似文献   

SH波水平入射穿越垂直断层隧道动力响应分析     

杜修力  苏驰翔  黄景琦  王媛  赵密  欧阳文龙 《震灾防御技术》2019,14(2):269-280

结合黏弹性人工边界的时域波动输入方法和显式有限元法，设计了含垂直断层三维场地的SH波输入方法。基于建立的输入方法，研究了垂直断层对隧道地震响应的影响，并通过自由场算例验证了该方法具有较好精度。数值模拟结果表明:对于断层迎波侧的隧道结构，断层会对其地震动响应产生显著的放大作用，对于断层逆波侧的隧道结构，断层会对其产生隔离地震动的作用；相对周围围岩，断层介质的剪切波速越小，其产生的放大效应和隔震效果也会越显著；断层宽度越小，其对隧道地震动响应的影响范围也就越小，但是断层宽度的变化对于断层两侧隧道的地震动响应的影响并不明显。  相似文献   

额河源流采金矿区不同恢复措施对矿区物种多样性和地上生物量的影响          下载免费PDF全文

许佳  祝晓瞳  苑塏烨 《干旱区地理》2019,42(3):581-589

以额河源流采金后废弃矿区为研究对象，于2011—2015年期间，通过采取不同恢复措施从被破坏矿区草地植物多样性和生物量的角度分析植被恢复效果。结果表明：（1）不同恢复措施促使各植物群落的物种数增加了5%～30%,说明采取恢复措施使得矿区生态环境得到了一定程度的恢复。（2）综合植被群落结构、盖度和地上生物量、物种多样性指数分析，措施A5（推平+圈羊）、A4（推平+补水）、A3（推平+覆土+黑加仑）较其他措施恢复效果更为显著。（3）通过对各样地植被丰富度指数（R）、Shannon Wiener指数(H′)、Simpson指数（D）、Pielou (Jsw)指数与地上生物量分别进行回归分析,发现指数曲线拟合关系最好。表明物种多样性与地上生物量均存在较显著的正相关关系(P <0.05)。本研究可为类似矿区的植被恢复与重建提供参考和借鉴。  相似文献   

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata     

Tingting Xu  Jay Gao  Giovanni Coco 《International journal of geographical information science》2019,33(10):1960-1983

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.  相似文献   

Evaluation of nonlinear models for precipitation forecasting     

Abazar Solgi  Vahid Nourani  Mohammad Bagherian Marzouni 《水文科学杂志》2017,62(16):2695-2704

Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting.  相似文献   

Remote sensing of water depths in shallow waters via artificial neural networks     

Özçelik Ceyhun  Arısoy Yalçın 《Estuarine, Coastal and Shelf Science》2010

Determination of the water depths in coastal zones is a common requirement for the majority of coastal engineering and coastal science applications. However, production of high quality bathymetric maps requires expensive field survey, high technology equipment and expert personnel. Remotely sensed images can be conveniently used to reduce the cost and labor needed for bathymetric measurements and to overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN) methodology is introduced in this study to derive bathymetric maps in shallow waters via remote sensing images and sample depth measurements. This methodology provides fast and practical solution for depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensing imagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly use image reflectance values in depth estimations, without refining depth-caused scatterings from other environmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluating nonlinear relationships between multi-band images and in-situ depth measurements, therefore leads more reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images were used to derive ANN based bathymetric maps of this study area. In-situ depth measurements were supplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster and one for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements were used to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed at the end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitive depth measurement requirements in bathymetric mapping especially for preliminary engineering application.  相似文献   

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model   总被引：10，自引：0，他引：10  

Biswajeet Pradhan  Ahmed M. Youssef  Renuganth Varathrajoo 《地球空间信息科学学报》2010,13(2):93-102

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.  相似文献   

Impact of artificial subsurface drainage on groundwater travel times and baseflow discharge in an agricultural watershed,Iowa (USA)     

Keith E. Schilling  Priyanka Jindal  Nandita B. Basu  Matthew J. Helmers 《水文研究》2012,26(20):3092-3100

Pollutant delivery through artificial subsurface drainage networks to streams is an important transport mechanism, yet the impact of drainage tiles on groundwater hydrology at the watershed scale has not been well documented. In this study, we developed a two‐dimensional, steady‐state groundwater flow model for a representative Iowa agricultural watershed to simulate the impact of tile drainage density and incision depth on groundwater travel times and proportion of baseflow contributed by tile drains. Varying tile drainage density from 0 to 0.0038 m^?1, while maintaining a constant tile incision depth at 1.2 m, resulted in the mean groundwater travel time to decrease exponentially from 40 years to 19 years and increased the tile contribution to baseflow from 0% to an upper bound of 37%. In contrast, varying tile depths from 0.3 to 2.7 m, while maintaining a constant tile drainage density of 0.0038 m^?1, caused mean travel times to decrease linearly from 22 to 18 years and increased the tile contribution to baseflow from 30% to 54% in a near‐linear manner. The decrease in the mean travel time was attributed to decrease in the saturated thickness of the aquifer with increasing drainage density and incision depth. Study results indicate that tile drainage affects fundamental watershed characteristics and should be taken into consideration when evaluating water and nitrate export from agricultural regions. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

Modelling combined open channel flow by artificial neural networks     

Han‐Chung Yang  Fi‐John Chang 《水文研究》2005,19(18):3747-3762

Combined open channel flow is encountered in many hydraulic engineering structures and processes, such as irrigation ditches and wastewater treatment facilities. Extensive experimental studies have conducted to investigate combined flow characteristics. Nevertheless, there is no simple relationship that can fully describe the velocity profiles in a turbulent flow. The artificial neural network (ANN) has great computational capability for solving various complex problems, such as function approximation. The main objective of this study is to evaluate the applicability of the ANN for simulating velocity profiles, velocity contours and estimating the discharges accordingly. The velocity profiles measured by an acoustic doppler velocimeter in the open channel of the Chihtan purification plant, Taipei, with different discharges at fixed measuring section and different depths are presented. The total number of data sets is 640 and the data sets are split into two subsets, i.e. training and validation sets. The backpropagation algorithm is used to construct the neural network. The results demonstrate that the velocity profiles can be modelled by the ANN, and the ANN constructed can nicely fit the velocity profiles and can precisely predict the discharges for the conditions investigated. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers? / Les techniques de réseaux de neurones artificiels sont-elles pertinentes pour estimer le coefficient de dispersion longitudinale en rivières?     

《水文科学杂志》2013,58(1)

Abstract

Abstract Accurate application of the longitudinal dispersion model requires that specially designed experimental studies are performed in the river reach under consideration. Such studies are usually very expensive, so in order to quantify the longitudinal dispersion coefficient, as an alternative approach, various researchers have proposed numerous empirical formulae based on hydraulic and morphometric characteristics. The results are presented of the application of artificial neural networks as a parameter estimation technique. Five different cases were considered with the network trained for different arrangements of input nodes, such as channel depth, channel width, cross-sectionally averaged water velocity, shear velocity and sinuosity index. In the case where the sinuosity index is included as an input node, the results turned out to be better than those presented by other authors.  相似文献