The Chiyoda basin is located in the Saga Prefecture of the Kyushu Island, Japan, and lies next to the tidal compartment of the Chikugo River, into which excess water in the basin is drained away. This basin has a total area of approximately 1100 ha and is a typical flat and low-lying agricultural area. The estimation of the water levels at the gates and along the main drainage canal is a crucial issue that has recently been the subject of much research. At these locations farmers and managers need to control the operation of the irrigation and drainage systems during periods of cultivation. An attempt has been made to apply a feed-forward artificial neural network (FFANN) to model and estimate the water levels in the main drainage canal. The study indicated that the artificial neural network (ANN) could successfully model the complex relationship between rainfall and water levels in this flat and low-lying agricultural area. Input variables and the model structure were selected and optimized by trial and error, and the accuracy of the model was then evaluated by comparing the simulated water levels with the observed ones during an irrigation period in July 2007. The water levels at two locations, located upstream and downstream of a main drainage canal, were investigated by using a time series at intervals of 20, 30, and 60 min. At these intervals, rainfall and tide water levels in the Chikugo River were measured, and the backward time-step numbers of the input variables of rainfall and tide water level were searched. For the upstream location, the optimal combination yielding good agreement between the observed and estimated water levels was obtained when the interval of the time series was 60 min. The number of backward time-steps of the input variables of rainfall and tide water level were 5 and 4, respectively. In contrast to the downstream location, the optimal combination was obtained for the interval time series of 20 min with 4 backward time-steps for both the input variables of rainfall and tide water level. The present study could provide farmers and managers with a useful tool for controlling water distribution in the drainage basin, and reduce the cost of installing water level observation points at many locations in the main drainage canal. 相似文献
Genetic relationship and variation of 29 accessions of teasle gourd (Momordica dioica Roxb.) and 1 accession of Momordica cochinchinensis Spreng. (wild relatives of teasle gourd) were examined by RAPD analysis using 44 dodecamer oligonucleotide primers. A total of 496 fragments were produced by 44 primers of which 95% bands were polymorphic. Using presence or absence of specific RAPD markers or combination of primers, 23 out of 30 accessions were identified. The genetic relatedness or genetic distance based on Nei and Li's genetic similarity varied from 0.86 to 0.65 with an average of 0.74 among 29 M. dioica accessions (when M. cochinchinensis excluded). In the phenetic dendrogram developed from cluster analysis using UPGMA method, M. cochinchinensis was out grouped as single accession, while others showing relatively weak grouping formed four groups. Clustering pattern did not demonstrate any relationship between geographical origin and genetic diversity. A DNA extraction method has been standardized. This is the first report of using RAPD techniques in teasle gourd. It was concluded that RAPD analysis is a useful tool for genotypic identification and estimation of genetic similarity in teasle gourd. 相似文献
Mammalian interferon (IFN)-alpha consists of a 23-amino acid signal peptide and a 166-amino acid mature protein. Feline (Fe) IFN-alpha has an extra unique molecule consisting of a 171-amino acid mature protein with a 5-amino acid insertion. We cloned eight new subtypes of cDNA encoding FeIFN- alpha from a feline epithelial cell line. Among all the FeIFN-alpha subtypes, including six that have previously been reported, the variations were found to be far less than those of IFN-alphas of other animals. 相似文献
Influences of a specific dietary nutrient on glucagon‐like peptide (GLP)‐1‐containing cells in the chicken intestine are not yet clear. Significance of dietary protein level on GLP‐1‐containing cells in the chicken ileum was investigated. Chickens fed control or experimental diets of varying protein levels were examined using immunohistochemical and morphometrical techniques. We show that the protein ingestion had an impact on the activities of GLP‐1‐immunoreactive cells in the chicken ileum. Weight gains declined with decreasing dietary crude protein (CP) levels, but no significant differences were detected in the daily feed intake and villous height. GLP‐1‐immunoreactive cells with a round or oval shape were frequently observed in the lower CP level groups (4.5% and 0%). Frequencies of occurrence of GLP‐1‐immunoreactive cells were 41.1 ± 4.1, 38.5 ± 4, 34.8 ± 3.1 and 34.3 ± 3.7 (cells/mm2, mean ± SD) for dietary CP level of 18%, 9%, 4.5% and 0% groups, respectively and significant differences were recognized between the control and lower CP level groups (P < 0.05). Multiple regression analysis indicated a significant correlation between the daily protein intake and frequencies of occurrence of GLP‐1‐immunoreactive cells. The protein ingestion is one of the signals that influence GLP‐1‐containing cells in the chicken small intestine. 相似文献
In the field of agriculture, development of evaluation techniques for environmental changes is urgently required for the purpose of finding a balance between growth in agricultural productivity and environmental considerations. The habitat evaluation procedures (HEP) constitute one technique for habitat assessment. While HEP is widely applied to estimate both habitat quality and quantity in an environment, it appears to be necessary to develop an accurate habitat prediction model in order to evaluate environments precisely. In fact, habitat selection by fish is affected by complicated interaction between multiple environmental factors, which makes it difficult to relate physical environments to habitat preference. In the present study, we utilize artificial neural networks (ANNs), which are commonly applied to model complex systems, to predict the habitat selection of Japanese medaka (Oryzias latipes) in agricultural canals. Considering the essential vagueness of fish behavior, fuzzy membership functions are introduced into the input layer, which advances ANN to fuzzy neural network (FNN). In addition, symmetric triangular fuzzy numbers are employed to account for uncertainty in measurement errors and dispersions of physical environment. The FNN model precisely predicts the habitat preference of Japanese medaka in an agricultural canal, and the results show a good agreement between the calculated and observed habitat suitability indices (HSI). Finally, the habitat quality of two different reaches at the same point in time is compared using HEP, with a view of suitable habitat for Japanese medaka. 相似文献
To effectively manage a eutrophic reservoir, short-term predictions of algae class-differentiated chlorophyll a (Chl-a) were conducted. The study adopted a three-layered feedforward artificial neural network using discrete water environment datasets collected through weekly observations from May to November over seven years (2012–2018). This network was constructed using supervised learning, and the available datasets of a certain observation day were set as the input variables to determine the total Chl-a, Chlorophyceae Chl-a, and cyanobacteria Chl-a that would exist after one week. From the viewpoint of the simplification of the network’s complexity to suppress overfitting, input variables were carefully selected by identifying the important variables related to the seasonal changes in Chlorophyceae and cyanobacteria and eliminating the duplicated expressions of water-quality parameters. However, network downsizing was found insufficient to suppress overfitting. To improve prediction accuracy, dropout was introduced, which stochastically deactivated some nodes in the input and hidden layers in the learning process. The analysis results showed that sufficient short-term predictions of total Chl-a and Chlorophyceae Chl-a may be achieved. The insufficient prediction accuracy of cyanobacteria Chl-a may be overcome using meteorological data as close as possible to the desired prediction day. Consequently, this model may serve as a useful tool for the management of eutrophic reservoirs because short-term predictions of the dominant phytoplankton can be achieved, and the necessary mitigation measures may be accordingly planned.