Production and purification of antibody by immunizing rabbit with rice tungro bacilliform and rice tungro spherical viruses

Rice tungro disease is the major disease caused by infection with the rice tungro bacilliform virus (RTBV) and rice tungro spherical virus (RTSV). In this study, New Zealand White rabbits were immunized with pure viruses for the production of antibodies against both species. The production of polyclonal antibodies against Tungro viral disease using ammonium sulfate precipitation and a protein A affinity column and their assessment are described. Two peaks were found from the protein A affinity column. Peak 1 represents the unbound compounds from the extracted serum and peak 2 represents antibody that bound to protein A, which was eluted using elution buffer. Peak 2 was collected for antibody titration. The amount of pure antibody in the titers was quantified by enzyme-linked immunosorbent assay (ELISA) to capture the tungro viruses. Antibody titer was analyzed by the ELISA method. For anti-RTBV, 1.696 mg/mL was highest at the second bleed and anti-RTSV was 2.3225 mg/mL was highest at the first bleed. These antibodies detected the tungro viral disease well and proved to be a potential probe for the detection of rice tungro disease

The Deep Mixing Method: Bearing Capacity Studies

Nowadays, several techniques are employed to improve the problem of carrying out construction in soft soils by increasing the strength of the soil foundation and reducing the settlement of the soil. Among these stabilizing techniques, the deep mixing method is regarded as the most popular. The deep mixing method is a soil modification method where the soil is mixed in situ with stabilizing agents, commonly soil–cement columns. It increases the strength of the soil, providing bearing resistance and improved settlement performance. Deep mixing is carried out in situ using a machine equipped with mixing blades mounted at the end of a tube that has a nozzle at the lower end. The stabilizer agent is injected into the soil via the nozzle using a pumping system so that it mixes with the soil as the blades are rotated. Throughout this paper, previous works by numerous researchers on deep mixing including laboratory work, full-scale field tests, analytical and numerical analyses related to bearing capacity are reviewed. The techniques and results used are discussed with the help of figures depicting charts, failure modes, and the model configuration setup. It was found that the deep mixing method is suitable for use with any type of soil and provides a better alternative to the existing method of improving soft clay ground, especially with regard to the soil bearing capacity. In addition, future research is needed to improve the use of the method for soil improvement in the construction industry

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set