Hamed Rezazadegan Tavakoli

Response surface methodology based on central composite design as a chemometric tool for optimization of dispersive-solidification liquid-liquid microextraction for speciation of inorganic arsenic in environmental water samples.

Dispersive-solidification liquid-liquid microextraction (DSLLME) coupled with electrothermal atomic absorption spectrometry (ETAAS) was developed for preconcentration and determination of inorganic arsenic (III, V) in water samples. At pH=1, As(III) formed complex with ammonium pyrrolidine dithiocarbamate (APDC) and extracted into the fine droplets of 1-dodecanol (extraction solvent) which were dispersed with ethanol (disperser solvent) into the water sample solution. After extraction, the organic phase was separated by centrifugation, and was solidified by transferring into an ice bath. The solidified solvent was transferred to a conical vial and melted quickly at room temperature. As(III) was determined in the melted organic phase while As(V) remained in the aqueous layer. Total inorganic As was determined after the reduction of the pentavalent forms of arsenic with sodium thiosulphate and potassium iodide. As(V) was calculated by difference between the concentration of total inorganic As and As(III). The variable of interest in the DSLLME method, such as the volume of extraction solvent and disperser solvent, pH, concentration of APDC (chelating agent), extraction time and salt effect, was optimized with the aid of chemometric approaches. First, in screening experiments, fractional factorial design (FFD) was used for selecting the variables which significantly affected the extraction procedure. Afterwards, the significant variables were optimized using response surface methodology (RSM) based on central composite design (CCD). In the optimum conditions, the proposed method has been successfully applied to the determination of inorganic arsenic in different environmental water samples and certified reference material (NIST RSM 1643e).

Chemometric assisted ultrasound leaching-solid phase extraction followed by dispersive-solidification liquid-liquid microextraction for determination of organophosphorus pesticides in soil samples.

Ultrasound leaching-solid phase extraction (USL-SPE) followed by dispersive-solidification liquid-liquid microextraction (DSLLME) was developed for preconcentration and determination of organophosphorus pesticides (OPPs) in soil samples prior gas chromatography-mass spectrometry analysis. At first, OPPs were ultrasonically leached from soil samples by using methanol. After centrifugation, the separated methanol was diluted to 50 mL with double-distillated water and passed through the C18 SPE cartridge. OPPs were eluted with 1 mL acetonitrile. Thus, 1 mL acetonitrile extract (disperser solvent) and 10 µL 1-undecanol (extraction solvent) were added to 5 mL double-distilled water and a DSLLME technique was applied. The variables of interest in the USL-SPE-DSLLME method were optimized with the aid of chemometric approaches. First, in screening experiments, fractional factorial design (FFD) was used for selecting the variables which significantly affected the extraction procedure. Afterwards, the significant variables were optimized using response surface methodology (RSM) based on central composite design (CCD). Under the optimum conditions, the enrichment factors were 6890-8830. The linear range was 0.025-625 ng g(-1) and limits of detection (LODs) were between 0.012 and 0.2 ng g(-1). The relative standard deviations (RSDs) were in the range of 4.06-8.9% (n=6). The relative recoveries of OPPs from different soil samples were 85-98%.

Application of multi-factorial experimental design to successfully model and optimize inorganic arsenic speciation in environmental water samples by ultrasound assisted emulsification of solidified floating organic drop microextraction

Ultrasound assisted emulsification of solidified floating organic drop microextraction (USAE-SFODME), combined with electrothermal atomic absorption spectrometry (ETAAS), was developed for the preconcentration and determination of trace amounts of As(III) and As(V) in environmental water samples. At pH = 1, As(III) formed complexes with ammonium pyrrolidine dithiocarbamate (APDC) and these were extracted into the fine droplets of 1-dodecanol (extraction solvent) which were dispersed with the aid of ultrasonication into the water sample solution. After extraction, the organic phase was separated by centrifugation, and was solidified by transferring into an ice bath. The solidified solvent was transferred to a vial and melted quickly at room temperature. As(III) was determined in the melted organic phase while As(V) remained in the aqueous layer. Total inorganic As was determined after the reduction of the pentavalent forms of arsenic with sodium thiosulphate and potassium iodide. The concentration of As(V) was calculated as the difference between the concentrations of total inorganic As and As(III). The variables of interest in the USAE-SFODME method, such as the volume of extraction solvent, pH, concentration of APDC (chelating agent), sonication time and salt effect were optimized using multivariate optimization approaches. A fractional factorial design (FFD) for screening and a central composite design for optimizing the significant variables were applied. A mathematical model was presented that successfully predicts changes in the response, depending on the input variables. Under the optimum conditions, the proposed method has been successfully used for the determination of inorganic arsenic in different environmental water samples and certified reference material (NIST RSM 1643e).

Bottom-up Fixation Prediction Using Unsupervised Hierarchical Models

Fixation prediction, also known as saliency modelling, has been a subject undergoing intense study in various contexts. In the context of assistive vision technologies, saliency modelling can be used for development of simulated prosthetic vision as part of the saliency-based cueing algorithms. In this paper, we present an unsupervised multi-scale hierarchical saliency model, which utilizes both local and global saliency pipelines. Motivated by bio-inspired vision findings, we employ features from image statistics. Contrary to previous research, which utilizes one-layer equivalent networks such as independent component analysis (ICA) or principle component analysis (PCA), we adopt independent subspace analysis (ISA), which is equivalent to a two-layer neural architecture. The advantage of ISA over ICA and PCA is robustness towards translation meanwhile being selective to frequency and rotation. We extended the ISA networks by stacking them together, as done in deep models, in order to obtain a hierarchical representation. Making a long story short, (1) we define a framework for unsupervised fixation prediction, exploiting local and global saliency concept which easily generalizes to a hierarchy of any depth. (2) we assess the usefulness of the hierarchical unsupervised features, (3) we adapt the framework for exploiting the features provided by pre-trained deep neural networks, (4) we compare the performance of different features and existing fixation prediction models on MIT1003, (5) we provide the benchmark results of our model on MIT300.

Mean-shift video tracking using color-LSN histogram

A texture based object tracking algorithm is presented. The algorithm is an extension to famous mean-shift tracking method. It does not rely on color histogram. It incorporates both color histogram and texture histogram information to model tracking target.

Study of gabor and local binary patterns for retinal image analysis

In this paper selection of proper feature for retinal vascular tissue segmentation is studied. Different features have been proposed for retinal vessel detection. One of the most famous features adapted is Gabor wavelet. Due to multi resolution property of Gabor, combination of scales can be used to extract features. However, similar features in feature vector would increase the possibility of inter-correlation and not an apt result would be achieved.

Automated center of radial distortion estimation, using active targets

In this paper an automated center of radial distortion estimation algorithm is explained. The method applied to the development of an autonomous camera calibration algorithm. The idea of active targets, which are controlled by calibration algorithm is the key to the autonomy in this work. n nThe proposed method decouples the center of radial distortion from other calibration parameters. It is shown that the proposed method approximates the center of radial distortion correctly. Also it helps to the accuracy of calibration framework.

Saliency Revisited: Analysis of Mouse Movements Versus Fixations

This paper revisits visual saliency prediction by evaluating the recent advancements in this field such as crowd-sourced mouse tracking-based databases and contextual annotations. We pursue a critical and quantitative approach towards some of the new challenges including the quality of mouse tracking versus eye tracking for model training and evaluation. We extend quantitative evaluation of models in order to incorporate contextual information by proposing an evaluation methodology that allows accounting for contextual factors such as text, faces, and object attributes. The proposed contextual evaluation scheme facilitates detailed analysis of models and helps identify their pros and cons. Through several experiments, we find that (1) mouse tracking data has lower inter-participant visual congruency and higher dispersion, compared to the eye tracking data, (2) mouse tracking data does not totally agree with eye tracking in general and in terms of different contextual regions in specific, and (3) mouse tracking data leads to acceptable results in training current existing models, and (4) mouse tracking data is less reliable for model selection and evaluation. The contextual evaluation also reveals that, among the studied models, there is no single model that performs best on all the tested annotations.

Ultra-preconcentration and determination of organophosphorus pesticides in soil samples by a combination of ultrasound assisted leaching-solid phase extraction and low-density solvent based dispersive liquid–liquid microextraction

An ultra-preconcentration technique composed of ultrasound assisted leaching-solid phase extraction (USAL-SPE) and low-density solvent based dispersive liquid–liquid microextraction (LDS-DLLME) coupled with gas chromatography-mass spectrometry (GC-MS) was developed for preconcentration and determination of organophosphorus pesticides (OPPs) in soil samples. Parameters that affect the efficiency of the procedure were investigated by a fractional factorial design (FFD). Afterwards, variables showing significant effects on the analytical responses were considered using response surface methodology (RSM) based on a central composite design (CCD). Under optimum conditions, the enrichment factors were 7215–9842, the linear range was 0.012–625 ng g−1 and limits of detection (LODs) were between 0.002 and 0.125 ng g−1. The relative standard deviations (RSDs) were in the range of 5.4–8.3% (n = 6), and the relative recoveries of OPPs from different soil samples were 84–98%. The proposed methodology constitutes a suitable approach for the analysis of OPPs in complex soil samples and requires minimum organic solvents consumption, sample manipulation and increased sample throughput.

Zinc hexacyanoferrate loaded mesoporous MCM-41 as a new adsorbent for cesium: equilibrium, kinetic and thermodynamic studies

AbstractMesoporous MCM-41 has been modified by loading of zinc hexacyanoferrate (ZnHCF) as a new adsorbent for cesium. The ZnHCF-loaded mesoporous MCM-41 (ZnHCF-MCM-41) was characterized using powder X-ray diffraction and nitrogen adsorption–desorption isotherm data, fourier transform infrared spectroscopy, scanning electron microscopy coupled with energy dispersive X-ray. The cesium removal performance of ZnHCF-MCM-41 from aqueous solutions has been studied, and the effect of the various parameters, such as initial pH value of solution, contact time, temperature, and initial concentration of the cesium ion on the adsorption efficiencies of ZnHCF-MCM-41 were investigated systematically by batch experiments. Adsorption kinetics was better described by the pseudo-second-order model and thermodynamic parameters indicated the adsorption process was feasible, endothermic, and spontaneous in nature. Adsorption isotherm of ZnHCF-MCM-41 was studied and the fitted results indicated that Langmuir model could well r...