Metal Impregnated Silica‐Carbon Materials from Rice Husk: A Versatile Sorbent for Toxic Organics and Inorganics in Water and Air

Carbon bearing materials derived from rice husk (RH) have long been recognized as sorbents for diverse chemicals – both organic and inorganic. This work presents an overview of studies demonstrating a single step process of carbonization of chemically pretreated RH to produce metal impregnated silica‐carbon char designated as silicarbon materials that can be utilized in sorbing out water‐borne organic and inorganic hazardous substances (such as phenol, hexavalent chromium, fluoride, and arsenic) and air‐borne volatile organic chemicals (such as acetone, chloroform, benzene, and pyridine). The metal‐impregnated silicarbon solids derived from RH appear to constitute renewable, low‐cost, user‐friendly, and efficient materials for control systems for indoor air contamination and for industrial as well as non‐industrial hazardous aqueous pollution.     

Determination of ultimate capacity of driven piles in cohesionless soil: A Multivariate Adaptive Regression Spline approach

The determination of ultimate capacity (Q) of driven piles in cohesionless soil is an important task in geotechnical engineering. This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction Q of driven piles in cohesionless soil. MARS uses length (L), angle of shear resistance of the soil around the shaft (?_shaft), angle of shear resistance of the soil at the tip of the pile (?_tip), area (A), and effective vertical stress at the tip of the pile as input variables. Q is the output of MARS. The results of MARS are compared with that of the Generalized Regression Neural Network model. An equation has been also presented based on the developed MARS. The results show the strong potential of MARS to be applied to geotechnical engineering as a regression tool. Copyright © 2011 John Wiley & Sons, Ltd.     

Application of Relevance Vector Machine for Prediction of Ultimate Capacity of Driven Piles in Cohesionless Soils

This paper examines the potential of relevance vector machine (RVM) in prediction of ultimate capacity of driven piles in cohesionless soils. RVM is a Bayesian framework for regression and classification with analogous sparsity properties to the support vector machine (SVM). In this study, RVM has been used as a regression tool. It can be seen as a probabilistic version of SVM. In this study, RVM model outperforms the artificial neural network (ANN) model based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also estimates the prediction variance. An equation has been developed for the prediction of ultimate capacity of driven piles in cohesionless soils based on the RVM model. The results show that the RVM model has the potential to be a practical tool for the prediction of ultimate capacity of driven piles in cohesionless soils.     

Least Square Support Vector Machine Applied to Slope Reliability Analysis

This paper investigates the feasibility of Least square support vector machine (LSSVM) model to cope the problem of implicit performance function during first order second moment (FOSM) method based slope reliability analysis. LSSVM is firmly based on the theory of statistical learning. In LSSVM, Vapnik’s ε -insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. Here, LSSVM has been used as a regression technique to approximate implicit performance functions. A slope example has been presented for illustrating the applicability of LSSVM based FOSM method. The developed LSSVM based FOSM has been compared with the artificial neural network (ANN) and least square method. The result shows that the approximation of LSSVM can be used in the FOSM method for slope reliability analysis.     

Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs

This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (E) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε‐insensitive loss function has been adopted. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E. The developed RVM model gives variance of the predicted E. A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E. Copyright © 2011 John Wiley & Sons, Ltd.     

Understanding the redshift evolution of the luminosity functions of Lyman α emitters

We present a semi-analytical model of star formation which explains simultaneously the observed ultraviolet (UV) luminosity function (LF) of high-redshift Lyman break galaxies (LBGs) and LFs of Lyman α emitters. We consider both models that use the Press–Schechter (PS) and Sheth–Tormen (ST) halo mass functions to calculate the abundances of dark matter haloes. The Lyman α LFs at   z ≲ 4  are well reproduced with only ≲10 per cent of the LBGs emitting Lyman α lines with rest equivalent width greater than the limiting equivalent width of the narrow band surveys. However, the observed LF at   z > 5  can be reproduced only when we assume that nearly all LBGs are Lyman α emitters. Thus, it appears that  4 < z < 5  marks the epoch when a clear change occurs in the physical properties of the high-redshift galaxies. As Lyman α escape depends on dust and gas kinematics of the interstellar medium (ISM), this could mean that on an average the ISM at   z > 5  could be less dusty, more clumpy and having more complex velocity field. All of these will enable easier escape of the Lyman α photons. At   z > 5  , the observed Lyman α LF are well reproduced with the evolution in the halo mass function along with very minor evolution in the physical properties of high-redshift galaxies. In particular, up to   z = 6.5  , we do not see the effect of evolving intergalactic medium opacity on the Lyman α escape from these galaxies.     

Viscous fluid cosmological models in the presence of the zero-mass scalar field

The curvature-free (k=0) FRW expanding cosmological model is developed corresponding to interacting viscous fluids and zero-mass scalar fields. In the absence of non-static scalar fields the model exhibits the existence of the initial singularity (Q=0). However, with non-negative coefficient of shear viscosity, in the presence of non-static scalar fields we find thatQ has a minimum value (0). If this epoch is treated as the initial one, it may be said that the presence of scalar fields avoids the initial singularity. Other physical behaviour that the model exhibit has been discussed.     

Slope stability analysis: a support vector machine approach

Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem. However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%.     

A New Blastability Index for Hard Roof Management in Blasting Gallery Method

The presence of hard and massive sandstone above the coal seam in underground coal mines often leads to delay in caving of overlying rock beds thereby causing excessive load on supports and posing danger to underground workings. The problem is more prominent in blasting gallery (BG) as well as longwall mining methods in Indian coal mines. Induced caving by blasting is a promising means for hard roof management in underground coal mines. Based on extensive studies and data collected from different mines in India, a Blastability Index (BI) has been developed which can be used for the classification of roof according to the degree of ease in caving by induced blasting. Different charge factors have also been suggested based on the Blastability Index. Due to wide change in the method of extractions, ??Cavability Index?? for longwall panel was found ineffective in case of BG method of working as well as bord and pillar working. For this reason, this proposed Blastability Index would be of immense help for caving of hard roof by induced blasting.