Removal of benzotriazole by Photo-Fenton like process using nano zero-valent iron: Response surface methodology with a Box-Behnken design

In this paper, the removal of benzotriazole (BTA) was investigated by a Photo-Fenton process using nano zero valent iron (NZVI) and optimization by response surface methodology based on Box-Behnken method. Effect of operating parameters affecting removal efficiency such as H2O2, NZVI, and BTA concentrations as well as pH was studied. All the experiments were performed in the presence of ultraviolet radiation. Predicted levels and BTA removal were found to be in good agreement with the experimental levels (R2 = 0. 9500). The optimal parameters were determined at 60 min reaction time, 15 mg L-1 BTA, 0.10 g L-1 NZVI, and 1.5 mmol L-1 H2O2 for Photo-Fenton-like reaction. NZVI was characterized using X-ray diffraction (XRD), transmission electron microscope (TEM) images, and scanning electron microscope (SEM) analysis

Heterogeneous oxidation of sulfacetamide in aquatic environment using ultrasonic and nano-fenton: Kinetics intermediates and bioassay test

In present study, degradation of sulfacetamide from a synthetic wastewater by sonofenton process using zero valent iron nanoparticles was investigated. The synthesized iron nanoparticles were characterized using transmission electron microscopy (TEM), UV-visible and X-ray diffraction pattern (XRD). The effect of various parameters, such as pH, nZVI dose, H2 O2 concentration and contact time was studied with batch experiments. The removal efficiency of sulfacetamide by US/nZVI/H2 Of process was about 91 for reaction time of 60 min, but less than 27 of chemical oxygen demand (COD) was removed. Kinetics studies showed that the degradation of sulfacetamide fitted well to the pseudo-second-order model. Using the LC/MS device, five intermediate from degradation of sulfacetamide were detected. The toxicity test, using micro toxicity study also showed that the effluent from the sono-Fenton reactor has a lower toxicity than sulfacetamide antibacterial. © 2019 Desalination Publications. All rights reserved

Sharp Response Microstrip LPF using Folded Stepped Impedance Open Stubs

A novel microstrip lowpass filter with high selectivity and wide stopband is proposed that comprises two lateral folded open stubs and a central mirrored semi-circle ended suppressing cell. The proposed filter has cut-off frequency of 2.28 GHz and is very compact. The stopband width with attenuation level more than -20 dB is equal to 5.47 fc and the transition band is only 0.14 GHz. This filter is designed, fabricated and measured and the simulated and measured results are in good agreemen

An investigation of the line of sight towards QSO PKS 0237-233

We present a detailed analysis of absorption systems along the line of sight towards QSO PKS 0237-233 using a high resolution spectrum of signal-to-noise ratio (SNR) ~ 60-80 obtained with the Ultraviolet and Visual Echelle Spectrograph mounted on the Very Large Telescope. This line of sight is known to show a remarkable overdensity of CIV systems that has been interpreted as revealing the presence of a supercluster of galaxies. A detailed analysis of each of these absorption systems is presented. In particular, for the z_abs = 1.6359 (with two components of logN(HI) = 18.45, 19.05) and z_abs = 1.6720 (logN(H I) = 19.78) sub-Damped Ly-alpha systems (sub-DLAs), we measure accurate abundances (resp. [O/H] = -1.63(0.07) and [Zn/H] = - 0.57(0.05) relative to solar). While the depletion of refractory elements onto dust grains in both sub-DLAs is not noteworthy, photoionization models show that ionization effects are important in a part of the absorbing gas of the sub-DLA at z_abs = 1.6359 (HI is 95 percent ionized) and in part of the gas of the sub-DLA at z_abs = 1.6359. The CIV clustering properties along the line of sight is studied in order to investigate the nature of the observed overdensity. We conclude that despite the unusually high number of CIV systems detected along the line of sight, there is no compelling evidence for the presence of a single unusual overdensity and that the situation is consistent with chance coincidence.Comment: Accepted for publication in MNRAS. 23 pages, 16 figures, 12 table

IPO valuation in an emerging market – a study in Iran

Purpose: This study aims to highlight the accuracy, performance and selection of the IPO valuation methods in the Islamic Republic of Iran's emerging market. Design/methodology/approach: We performed accurate ex-ante evaluations based on a pre-IPO dataset obtained from valuation institutions. We considered valuation methods through correlations, Mann-Whitney U tests and regression analysis, using a sample of 83 IPOs from January 2017 to March 2021. Findings: We found that the Dividend Discount Model (DDM) was the most popular in Iran. Even after controlling firm characteristics and market circumstances, the IPO price was highly correlated to pre-IPO reports' estimates. The results showed that firms' age, size and profitability affected the selection of valuation methods. The valuers did not apply forward P/E in a volatile market. Firm size affected the weights assigned to Free Cash Flow to the Firm (FCFF), and the valuers considered the Asset-in-Place (AIP) intensity to determine the weights of DDM, P/E and Net Asset Value (NAV), and they mainly employed the P/E to value old firms. Finally, this study estimated the accuracy of the pre-IPO report at 61% and found the highest accuracy to be associated with DDM. Originality/Value: IPO pricing in emerging markets constitutes a more significant dilemma than in developed markets. This paper provides empirical evidence of IPO pricing focusing on valuation methods used in the context of an emerging market – the Islamic Republic of Iran

Human action recognition via skeletal and depth based feature fusion

This paper addresses the problem of recognizing human actions captured with depth cameras. Human action recognition is a challenging task as the articulated action data is high dimensional in both spatial and temporal domains. An effective approach to handle this complexity is to divide human body into different body parts according to human skeletal joint positions, and performs recognition based on these part-based feature descriptors. Since different types of features could share some similar hidden structures, and different actions may be well characterized by properties common to all features (sharable structure) and those specific to a feature (specific structure), we propose a joint group sparse regression-based learning method to model each action. Our method can mine the sharable and specific structures among its part-based multiple features meanwhile imposing the importance of these part-based feature structures by joint group sparse regularization, in favor of discriminative part-based feature structure selection. To represent the dynamics and appearance of the human body parts, we employ part-based multiple features extracted from skeleton and depth data respectively. Then, using the group sparse regularization techniques, we have derived an algorithm for mining the key part-based features in the proposed learning framework. The resulting features derived from the learnt weight matrices are more discriminative for multi-task classification. Through extensive experiments on three public datasets, we demonstrate that our approach outperforms existing methods