Enzymic and in sacco methods to estimate rumen degradation of food protein in cattle

BACKGROUND: Two factorial studies compared enzymic and in sacco methods to estimate degradation of ruminant foods. Enzyme degradation (in vitro = enzyme) was determined from the release of leucine‐equivalent amino acid (LA) crude protein (CP) from sunflower meal (SF), maize gluten meal (MG), distillers' dark grain (DG) and field beans (FB) after their separate incubations with Streptomyces griseus enzyme for 0–24 h. In sacco crude protein (CP) degradation of these foods was estimated during washing (0 h) and rumen incubations in fistulated cows for 2–24 h. The LA data were expressed as g LA per either kg of CP (LACP) or acid‐hydrolysable LA (HLA) of each food and compared with in sacco data. RESULTS: These methods showed comparable degradation with time (P < 0.01). The in sacco and HLA were greater than LACP for all foods except MG where in sacco value was either lower or equal to LACP depending upon the incubation time (P > 0.05 or P < 0.05). Conversely, HLA was significantly (P < 0.01) greater than LACP from 2 h onwards. At 0 h, in sacco values were significantly greater than those of enzyme for SF, DG and FB (P < 0.05) but not for MG. The foods differed significantly for degradation constants (a, b, c) in each method (P < 0.05). CONCLUSIONS: Despite variations between in sacco and enzyme estimates for different foods, the relationships between these estimates suggest that the HLA enzyme method has the potential to estimate food degradation. Copyright © 2007 Society of Chemical Industry     

Window materials for high power gyrotron

The room temperature application of sapphire as window material at higher frequencies is not feasible since its absorption coefficient increases almost linearly with increasing frequency in the millimeter wavelength region. At cryogenic temperature the absorption coefficient value decreases only by a few factors (factor of 2 to 3) in the 90 – 200 GHz region. The earlier reported temperature squared dependence (decrease) in the absorption coefficient or the loss tangent value is totally absent in our broad band continuous wave data we are reporting here (at 6.5 K, 35K, 77K and 300K) and one we reported at conferences earlier. Our results are verified by another technique. We utilize our precision millimeter wave dispersive Fourier transform spectroscopic techniques at room temperature and at cryogenic temperatures The extra high resistivity single crystal compensated silicon is no doubt the lowest loss material available at room temperature in the entire millimeter wavelength region At higher millimeter wave frequencies an extra high resistivity silicon window or an window made with extra high resistivity silicon coated with diamond film would certainly make a better candidate in the future. A single free standing synthetic diamond window seems to have higher absorption coefficient values at millimeter wavelength region at this time although it is claimed that it possesses good mechanical strength and higher thermal conductivity characteristics. It certainly does not rule out the use of diamond film on a single crystal high resistivity silicon to improve its mechanical strength and thermal conductivity     

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

     

Priority-based joint EDF–RM scheduling algorithm for individual real-time task on distributed systems

The Journal of Supercomputing - Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise...     

Modeling and control of a finger-like mechanism using bending shape memory alloys

Microsystem Technologies - In this research a biologically inspired finger-like mechanism similar to human musculoskeletal system is developed based on Shape Memory Alloys (SMAs). SMA actuators are...     

Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization

Neural Computing and Applications - Cryptography often involves substituting (and converting) the secret information into dummy data so that it could reach the desired destination without leakage....     

A systematic review on emperor penguin optimizer

Neural Computing and Applications - Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold....     

Feature Selection and Extraction Methods for Power Systems Transient Stability Assessment Employing Computational Intelligence Techniques

One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia’s power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.     

Effects of Solvent Exchange Period and Heat Treatment on Physical and Chemical Properties of Rice Husk Derived Silica Aerogels

In this study, hydrophobic silica aerogels were synthesized from rice husk ash-derived sodium silicate through sol-gel processing, solvent exchange, surface modification and ambient pressure drying. By volume, 10% of trimethylchlorosilane (TMCS) in 90% of n-hexane was used as a hydrophobic solution in the surface modification process. The physical and chemical properties of silica aerogels were characterized by density and porosity measurements, scanning electron microscopy (SEM), Fourier transforms infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller theory (BET) and dynamic scanning calorimetry (DSC). The hydrogels prepared were in the form of 2.5 ± 0.5 mm beads and then converted into alcogels through solvent exchange with ethanol for repetition of 3, 6 and 9 days. It is found that the optimal quality of silica aerogels with the BET surface area as high as 668.82 m²/g was obtained from the alcogels of the solvent exchange period of 9 days. Depending on the size of the gel’s block, a longer solvent exchange period will ensure adequate removal of pore water. Post heat treatment on silica aerogels obtained from the 9 days of solvent exchange at 200, 300 and 400 °C for 2 h results in slight decreased of aerogel’s density from 0.048 g/cm³ to 0.039 g/cm³ and the hydrophobicity of the aerogels is decreased above 380 °C as confirmed by DSC analysis.