Showing 4 results for Rumen
A. Nikkhah, M. Alikhani, H. Amanlou, A. Samie,
Volume 7, Issue 1 (4-2003)
Abstract
A ruminal in situ experiment using three fistulated ewes was conducted to determine dry matter (DM) and crude protein (CP) degradation of steam–flaked broomcorn (SFBr), ground broomcorn (GBr) and ground barley (GB). Grain samples were suspended in the rumen of sheep for 0, 2, 4, 8, 16, 24 and 48 h. Nylon bags were washed with tap water after removal. Effective degradability of DM at outflow rates of k = 0.05 and k = 0.08 h-1 was significantly higher for SFBr than for GBr (59 and 53% vs. 43 and 35%). SFBr has considerably higher soluble DM than GBr and GB. Insoluble DM of SFBr was lower than that of GBr and GB. Solubility of CP in broomcorn grain was significantly decreased by steam–flaking, but degradation rate of insoluble CP was not altered. Results from this study showed that SFBr supplies the major source of availabe nutrients for rumen microorganisms compared with GBr. In other words, using the most efficient processing method for ruminants will be necessary.
T. Ghoorchi, S. Rahimi, M. Rezaeian, G. R. Ghorbani,
Volume 7, Issue 2 (7-2003)
Abstract
An experiment was carried out to estimate the potential activity of rumen anaerobic fungi in the degradation of dry matter and fiber of feeds. Samples of wheat bran, bagasse, cotton seed, alfalfa and corn silage were used as the substrates to culture rumen fungi which were isolated from a fistulated Shal sheep. Loss percentages of dry matter (DML), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent (ADL), cellulose, and hemicellulose of samples were measured after 0, 3, 6 and 9 days of incubation.
Dry matter and NDF loss of substrates varied from 10.6 % to 29.4% and 11.7% to 48.7% after 9 days of fungi growth. The highest and lowest DML and NDF were related to alfalfa and bagasses, respectively. The highest values for the ADF loss (39%), hemicellulose loss (65.6%) and cellulose loss (55.6%) were measured from alfalfa. The results indicated that rumen anaerobic fungi have the ability of degrading dry matter and fiber from different types of feed.
S. M. A. Zomorodian, H. Chochi,
Volume 16, Issue 62 (3-2013)
Abstract
Excess pore water pressure in clay core dams during construction and primary filling reservoir (first impounding) causes initiation and progression of hydraulic fracture. In this research, the instrumentation data during construction and first filling reservoir (first impounding) was analyzed. It measured internal deformations, pore water pressures and total vertical stresses and compared with the analysis results in Masjed-e-Soleiman dam. To do this analysis, GEOSTUDIO 2004 V. 6.02 software was used. The staged construction of the dam was the model in the form of 2D coupled consolidation. The Non-linear elastic model for the core material and Linear Elastic model for other zones were incorporated into the models. For exact assessment and to obtain correct parameters of the constitutive model, the triaxial tests were performed on the core material of Masjed-e-Soleiman Dam and acceptable results were obtained.
H. Hakimi Khansar, A. Hosseinzadeh Dalir, J. Parsa, J. Shiri,
Volume 26, Issue 2 (9-2022)
Abstract
Accurate prediction of pore water pressure in the body of earth dams during construction with accurate methods is one of the most important components in managing the stability of earth dams. The main objective of this research is to develop hybrid models based on fuzzy neural inference systems and meta-heuristic optimization algorithms. In this regard, the fuzzy neural inference system and optimizing meta-heuristic algorithms including genetic algorithms (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), ant colony optimization algorithm (ACOR), harmony search algorithm (HS), imperialist competitive algorithm (ICA), firefly algorithm (FA), and grey wolf optimizer algorithm (GWO) were used to improve training system. Three features including fill level, dam construction time, and reservoir level (dewatering) obtained from the dam instrumentation were selected as the inputs of hybrid models. The results showed that the hybrid model of the genetic algorithm in the test period had the best performance compared to other optimization algorithms with values of R2, RMSE, NRMSE, and MAE equal to 0.9540, 0.0866, 0.1232, and 0.0345, respectively. Also, ANFIS-GA, ANFIS-PSO, ANFIS-ICA, and ANFIS-HS hybrid algorithms performed better than ANFIS-GWO, ANFIS-FA, ANFIS-ACORE, and ANFIS-DE in improving ANFIS network training and predicting pore water pressure in the body earthen dams at the time of construction.
