Experimental investigation and thermodynamic analysis of application of hybrid nanofluid in a parabolic solar trough collector.

In this research, thermal modeling has been done to investigate the effect of nanofluid on the performance of the linear parabolic collector. Therminol vapor/liquid phase fluid (VP-1) has been used as a base fluid; iron oxide nanoparticles have been used to produce mono-nanofluid; and iron oxide multi-walled carbon nanotubes nanocomposite has been used as nanoparticles to produce hybrid nanofluid. The fluid flow inside the absorber tube of the collector is assumed to be turbulent. The results show that when hybrid nanofluid and mono-nanofluid are used, the energy and exergy efficiencies of the collector are higher than those for the conditions of using the base fluid, but their amount is slightly lower with the use of hybrid nanofluid than when the working fluid is mono-nanofluid. According to the obtained results, the highest energy efficiency of the linear parabolic collector using nanofluid and mono-nanofluid is 70.2% and 70.4%, respectively, and the highest exergy efficiency is 35.7% and 35.9%, respectively. Despite this, the friction coefficient of mono-nanofluid compared to hybrid nanofluid was obtained on average about 9% higher. The results showed that the criterion for evaluating the performance of the collector (hydrodynamic thermal efficiency) when hybrid nanofluid is used is more than when mono-nanofluid is used.

Prediction models based on soil properties for evaluating the heavy metal uptake into Hordeum vulgare L. grown in agricultural soils amended with different rates of sewage sludge.

The current study aims at forming new prediction models to be employed in the approximating the possible uptake of a range of 10 heavy metals (HMs) (Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb and Zn) by Hordeum vulgare tissues including roots, shoots and grains following its growth in soil amended with sewage sludge (SS) using conditions employed in greenhouses. The present study determined an insignificant difference between the actual and predicted quantities of the HMs in the three tissues using t values. The majority of the predicted quantities of the HMs were acceptable with the exception of Cd in the shoots, Cu in grains and Pb in roots. Consequently, it is possible to use these models in assessing the cultivation of barley plants in soil amended with SS in a safe way, while simultaneously monitoring any potential risks to the health of humans.

Temporal Potential of Phragmites australis as a Phytoremediator to Remove Ni and Pb from Water and Sediment in Lake Burullus, Egypt.

In the current work, we investigated the concentration of Ni and Pb in different organs of Phragmites australis to evaluate its potential application as a phytoremediator to remove these two metals from contaminated water and sediment in Lake Burullus (a Ramsar site in Egypt). Above- and below-ground biomass of P. australis, water and sediment were sampled monthly for 1 year at six sites of Lake Burullus (three sites represent each of the northern and southern parts of the lake) using six randomly distributed quadrats (each of 0.5 × 0.5 m) at each sampling site. Significant variation was detected for Ni and Pb concentrations in the sediments and waters between the northern and southern sites of the lake. The biomass of P. australis in the southern sites was greater than that in the northern sites; in addition, the above-ground biomass was higher than the below-ground biomass. The above-ground organs accumulated higher concentrations of Ni and Pb than the below-ground organs. The Ni and Pb standing stocks data indicated that the organs of P. australis extracted higher amounts of Ni and Pb per its area from the southern rather than the northern sites. In the current study, the Ni and Pb above-ground standing stocks increased from the early growing season (February) and reached its peak during August and then decreased. The highest monthly Ni and Pb standing stock (18.2 and 18.4 g m- 2, respectively) was recorded in the above-ground organs of plants in the southern sites in August. The bioaccumulation factor of Ni was 157.6 and 153.4 in the northern and southern sites, respectively, whereas that of Pb was 175.3 and 158.3. The translocation factor of Ni and Pb from the below- to above-ground organs was generally > 1. Thus, this reed species is a potential candidate for Ni and Pb phytoextraction. Based on our results, P. australis could be used for the extraction of Ni and Pb to reduce the pollution in Lake Burullus, if the above-ground biomass is harvested at its maximum value in August, as was the case regarding the maximum standing stock of Ni and Pb.

Modified Liu estimators in the linear regression model: An application to Tobacco data.

BACKGROUND: The problem of multicollinearity in multiple linear regression models arises when the predictor variables are correlated among each other. The variance of the ordinary least squared estimator become unstable in such situation. In order to mitigate the problem of multicollinearity, Liu regression is widely used as a biased method of estimation with shrinkage parameter 'd'. The optimal value of shrinkage parameter plays a vital role in bias-variance trade-off. LIMITATION: Several estimators are available in literature for the estimation of shrinkage parameter. But the existing estimators do not perform well in terms of smaller mean squared error when the problem of multicollinearity is high or severe. METHODOLOGY: In this paper, some new estimators for the shrinkage parameter are proposed. The proposed estimators are the class of estimators that are based on quantile of the regression coefficients. The performance of the new estimators is compared with the existing estimators through Monte Carlo simulation. Mean squared error and mean absolute error is considered as evaluation criteria of the estimators. Tobacco dataset is used as an application to illustrate the benefits of the new estimators and support the simulation results. FINDINGS: The new estimators outperform the existing estimators in most of the considered scenarios including high and severe cases of multicollinearity. 95% mean prediction interval of all the estimators is also computed for the Tobacco data. The new estimators give the best mean prediction interval among all other estimators. THE IMPLICATIONS OF THE FINDINGS: We recommend the use of new estimators to practitioners when the problem of high to severe multicollinearity exists among the predictor variables.