Equilibrium,Thermodynamic, and Kinetic Studies on Lead (II) Biosorption from Aqueous Solution by Saccharomyces cerevisiae Biomass

This paper presents a biosorption procedure for the preconcentration of Pb²⁺ ions using Saccharomyces cerevisiae biomass. The influence of several factors including pH, biomass dosage, contact time, and temperature on biosorption efficiency were optimized. At optimum value of all the equilibrium, thermodynamic, and kinetic parameters of Pb²⁺ ion biosorption was investigated by testing the Langmuir and Freundlich models and first and second order kinetic models were applied. The biosorption capacity of S. cerevisiae biomass was determined 89.6 mg/g, while the retained Pb²⁺ ions by S. cerevisiae were reversibly eluted using 5 mol/L HNO₃. Due to the high stability of S. cerevisiae the applied biomass can be used successively ten times with a slightly decrease (about 20%) in the recovery of Pb²⁺ ions. The calculated thermodynamic parameters, ΔG°, ΔH°, and ΔS° showed that the biosorption of Pb²⁺ ion onto S. cerevisiae biomass was feasible, spontaneous, and endothermic under examined conditions. The results of kinetic analysis showed that the biosorption processes of Pb²⁺ ions onto S. cerevisiae biomass followed pseudo second order kinetics.     

Regional scale rainfall–runoff modeling using VARX–MGARCH approach

Multivariate time series modeling approaches are known as useful tools for describing, simulating, and forecasting hydrologic variables as well as their changes over the time. These approaches also have temporal and cross-sectional spatial dependence in multiple measurements. Although the application of multivariate linear and nonlinear time series approaches such as vector autoregressive with eXogenous variables (VARX) and multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models are commonly used in financial and economic sciences, these approaches have not been extensively used in hydrology and water resources engineering. This study employed VARX and VARX–MGARCH approaches in modeling mean and conditional heteroscedasticity of daily rainfall and runoff records in the basin of Zarrineh Rood Dam, Iran. Bivariate diagonal VECH (DVECH) model, as a main type of MGARCH, shows how the conditional variance–covariance and conditional correlation structure vary over the time between residuals series of the fitted VARX. For this purpose, five model fits, which consider different combinations of twofold rainfall and runoff, including both upstream and downstream stations, have been investigated in the present study. The VARX model, with different orders, was applied to the daily rainfall–runoff process of the study area in each of these model fits. The Portmanteau test revealed the existence of conditional heteroscedasticity in the twofold residuals of fitted VARX models. Therefore, the VARX–DVECH model is proposed to capture the heteroscedasticity existing in the daily rainfall–runoff process. The bivariate DVECH model indicated both short-run and long-run persistency in the conditional variance–covariance matrix related to the twofold innovations of rainfall–runoff processes. Furthermore, the evaluation criteria for the VARX–DVECH model revealed the improvement of VARX model performance.     

Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model

Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.     

Nonlinear modeling of cyclic response of RC beam–column joints reinforced by plain bars

Experience of previous earthquakes shows that a considerable portion of buildings reinforced with plain bars sustain relatively large damages especially at the beam–column joints where the damages are mostly caused by either diagonal shear cracks or intersectional cracks caused by bar slippage. While previous works mainly focus on shear failure mode, in this study, the emphasis is placed on slip based cracks as the dominant failure mode. A systematic procedure is introduced to predict the dominant failure mode at the joint which is based on the dimensional properties, reinforcement details, and axial and shear load at the joint. In addition, a relatively simple and efficient nonlinear model is proposed to simulate pre- and post-elastic behavior of the joints which fail under bar slippage mode. In this model, beam and column components are represented by linear elastic elements, dimensions of the joint panel are defined by rigid elements, and effect of slip is taken into account by a nonlinear rotational spring at the end of the beam. The proposed method is validated by experimental results for both internal and external joints .     

New magnitude scaling relations for earthquake early warning in the Alborz region,Iran

Rapid magnitude estimation relations for earthquake early warning systems in the Alborz region have been developed based on the initial first seconds of the P-wave arrival. For this purpose, a total of 717 accelerograms recorded by the Building and Housing Research Center in the Alborz region with the magnitude (M_w) range of 4.8–6.5 in the period between 1995 and 2013 were employed. Average ground motion period (\( \tau_{\text{c}} \)) and peak displacement (\( P_{\text{d}} \)) in different time windows from the P-wave arrival were calculated, and their relation with magnitude was examined. Four earthquakes that were excluded from the analysis process were used to validate the results, and the estimated magnitudes were found to be in good agreement with the observed ones. The results show that using the proposed relations for the Alborz region, earthquake magnitude could be estimated with acceptable accuracy even after 1 s of the P-wave arrival.     

A new solution to estimate the time delay on the topographic site using time domain 3D boundary element method

This study focuses on investigating spatial variation of ground motion that has great influence on the dynamic behavior of the large structures located on the surface topography. One of the most effective parameters on the spatial variation of ground motion is the difference between the arrival time of seismic waves in different points located on the abutments. In this research, a three-dimensional model of the Pacoima Dam site is prepared. The time domain 3 D boundary element method is used to apply non-uniform excitation at the dam supports. This model is subjected to vertically propagating incident SH and P waves. The time delay can be characterized by calculating the value of the time delay for which the cross-correlation between two records is maximized. Finally, to obtain the time delay in a topographic site, a function considering effective parameters such as the height from the canyon base, wave velocity and predominant frequency, is presented. Furthermore, a code was developed for generating the spatially variation of seismic ground motions. The results show that the proposed functions have an acceptable accuracy in estimating the time delay to generate non-uniform ground motion.     

Using Dempster–Shafer theory to model earthquake events

Natural Hazards - In this study, Dempster–Shafer theory (DST) is integrated into a geographic information system to model vulnerability of the land surface to earthquake events in...     

Aliphatic and aromatic hydrocarbons in coastal Caspian Sea sediments

This investigation represents the first extensive study of the spatial distribution and sources of aliphatic (n-alkanes and unresolved complex mixture of fossil hydrocarbons) and polycyclic aromatic hydrocarbons (PAHs) in coastal sediments from the Caspian Sea. PAH concentrations, n-alkanes and biomarker profiles all suggested that there was limited petrogenic contamination in the shallow North Caspian Sea sediments, which are coarse with a low total organic carbon content. In contrast, moderate to high petrogenic contamination was found in the South Caspian Sea, in particular in the offshore oil fields near Baku, Azerbaijan. Contaminant patterns indicated that the PAHs were mainly from fossil sources, with higher contributions of pyrolytic only near industrialized and urban areas. A high contribution of perylene, a geochemically derived PAH, to the total PAHs was found in the west and south at sites influenced by the Kura, Safid Rud, Terek, Sulak and Samur Rivers.     

Endmember orthonormal mapping in hyperspectral mixture analysis to address endmember variability

Spectral unmixing estimates the abundance of each endmember at every pixel of a hyperspectral image. Each material in traditional unmixing algorithms is represented through a constant spectral signature. However, endmember variability always exists due to environmental, atmospheric, and temporal conditions, which leads to poor accuracy of the estimated abundances. This paper proposes a new unmixing algorithm based on a new linear transformation called endmember orthonormal mapping (EOM) to overcome the aforementioned problem. The EOM transformation maps original spectral space to a new EOM space to reduce endmember variability. In the original spectral space, each material is represented by a set of spectra (endmember set) which is extracted using the automated endmember bundles (AEB) method. The EOM transforms each endmember set to a vector in the EOM space so that these vectors are orthonormal. On account of orthonormalized endmembers, the condition number of the mixing matrix in the EOM space reduces. Furthermore, we consider the noise term as an additional virtual endmember set mapped to a vector that is orthogonal to other endmembers. As a result, a promising unmixing accuracy is obtained through applying the least squares abundance estimation in the subspace orthogonal to noise. Experimental results of both synthetic and real hyperspectral images demonstrate that the proposed algorithms provide much enhanced performance compared with the state-of-the-art algorithms.     

Prediction and controlling of flyrock in blasting operation using artificial neural network

Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine.