Sharif Digital Repository

A fast high-performance liquid chromatography-diode array detection (HPLC-DAD) approach combined to solid phase extraction (SPE) as a pre-concentration step is developed for simultaneous determination of five selected pharmaceuticals (carbamazepine, naproxen, diclofenac, gemfibrozil and mefenamic acid) in water samples. The effective factors on the efficiency of SPE procedure are optimized using faced-centered central composite design (FCCD). In addition, multi-response optimization by using Derringer's desirability function is used to find the optimum experimental conditions for extraction of analytes from well and river waters. Due to the complexity of water matrices and the presence of... 

Synchronization behavior of electroencephalographic (EEG) signals is important for decoding information processing in the human brain. Modern multichannel EEG allows a transition from traditional measurements of synchronization in pairs of EEG signals to whole-brain synchronization maps. The latter can be based on bivariate measures (BM) via averaging over pair-wise values or, alternatively, on multivariate measures (MM), which directly ascribe a single value to the synchronization in a group. In order to compare BM versus MM, we applied nine different estimators to simulated multivariate time series with known parameters and to real EEGs. We found widespread correlations between BM and MM,... 

In this paper, a heuristic threshold policy is developed to detect and classify the states of a multivariate quality control system. In this approach, a probability measure called belief is first assigned to the quality characteristics and then the posterior belief of out-of-control characteristics is updated by taking new observations and using a Bayesian rule. If the posterior belief is more than a decision threshold, called minimum acceptable belief determined using a heuristic threshold policy, then the corresponding quality characteristic is classified out-of-control. Besides using a different approach, the main difference between the current research and previous works is that the... 

A new approach is developed in this paper to detect general mean shifts of multivariate quality control systems and to determine the quality characteristic(s) responsible for the shift. This approach takes advantage of both a decomposition method and an EWMA-based control statistics that are employed for multivariate normal distributions. In order to evaluate the performance of the proposed methodology, simulation studies are provided to estimate the in- and out-of-control average run lengths under different mean and variance shift scenarios. Simulation experiments are also given to compare the performances of the proposed procedure with the ones of the well-known MEWMA and MCUSUM methods.... 

Temporal synchronization of neuronal activity plays an important role in various brain functions such as binding, cognition, information processing, and computation. Patients suffering from disorders such as Alzheimer’s disease or schizophrenia show abnormality in the synchronization patterns. Electroencephalography (EEG) is a cheap, non-invasive, and easy-to-use method with fine temporal resolution. Modern multichannel EEG data are increasingly being used in brain studies. Traditional approaches for identifying synchronous activity in EEG are through univariate techniques such as power spectral density or bivariate techniques such as coherence. In this paper, we review two methods for... 

Enterprise Resource Planning systems (ERP) play a significant role in the management of businesses processes. Determining the influential factors that are positioned behind a successful ERP implementation is critical to gain the most value-added from competitive potentials of such systems. The driver of current research is to gain a deeper understanding of the various subjective criteria to measure ERP implementation success with the aim of enhancing the chance success through investigating ERP implementation performance indicators. By exploring the international literature, a comprehensive list of potential success factors of ERP systems is identified which are complementary and critical to... 

Multivariate curve resolution (MCR) and multivariate clustering methods along with other chemometric methods are proposed to improve the analysis of gas chromatography-mass spectrometry (GC-MS) fingerprints of secondary metabolites in citrus fruits peels. In this way, chromatographic problems such as baseline/background contribution, low S/N peaks, asymmetric peaks, retention time shifts, and co-elution (overlapped and embedded peaks) occurred during GC-MS analysis of chromatographic fingerprints are solved using the proposed strategy. In this study, first, informative GC-MS fingerprints of citrus secondary metabolites are generated and then, whole data sets are segmented to some... 

The main aim of this work was to assess the ability of Bayesian multivariate adaptive regression splines (BMARS) and Bayesian radial basis function (BRBF) techniques for modelling the gas chromatographic retention indices of volatile components of Artemisia species. A diverse set of molecular descriptors was calculated and used as descriptor pool for modelling the retention indices. The ability of BMARS and BRBF techniques was explored for the selection of the most relevant descriptors and proper basis functions for modelling. The results revealed that BRBF technique is more reproducible than BMARS for modelling the retention indices and can be used as a method for variable selection and... 

In multi-attribute process monitoring, when a control chart signals an out-of-control condition indicating the existence of a special cause, knowing when the process has really changed (the change point) accelerates the identification of the source of the special cause and makes the corrective measures to be employed sooner. This, of course, results in a considerable amount of savings in time and money. Since many real world multi-attribute processes are Poisson and most process changes are step-change, a new method is proposed, in this paper, to derive the maximum likelihood estimator of the time of a step-change in the mean vector of multivariate Poisson processes. In this method, two... 

Control charts are the best tools to monitor main process parameters, and the Multivariate Exponentially Weighted Moving Average, MEWMA, type of this tool is used when there are several correlated quality characteristics to be monitored simultaneously where detecting small deviations of the characteristics is desired. In this paper, the models of both the economic and the economic-statistical design problems of MEWMA control charts are solved by a Particle Swarm Optimization (PSO) approach. The comparison study between the economic and the economic-statistical designs shows better statistical performances of the economic-statistical design with negligible increase in cost. Furthermore, in... 

This article presents experimental results on the electrophoretic deposition (EPD) of bioresorbable chitosan/45S5 Bioglass® composite coatings for orthopaedic implants based on the Taguchi design of experiments (DOE) approach. The influence of EPD parameters including Bioglass® concentration, electric voltage and deposition time on deposition yield was studied by an orthogonal Taguchi array of L18 type. Multivariate analysis of variance (MANOVA) and regression analysis based on the partial least-square method were used to identify the significant factors affecting the deposition yield and its stability during constant-voltage EPD. The coatings were characterised by high resolution scanning... 

Electroweak production of the top quark is measured for the first time in pp collisions at √s=7TeV, using a data set collected with the CMS detector at the LHC and corresponding to an integrated luminosity of 36pb-1. With an event selection optimized for t-channel production, two complementary analyses are performed. The first one exploits the special angular properties of the signal, together with background estimates from the data. The second approach uses a multivariate analysis technique to probe the compatibility with signal topology expected from electroweak top-quark production. The combined measurement of the cross section is 83.6±29.8(stat+syst)±3.3(lumi) pb, consistent with the... 

Essential oils (EOs) are valuable natural products that are popular nowadays in the world due to their effects on the health conditions of human beings and their role in preventing and curing diseases. In addition, EOs have a broad range of applications in foods, perfumes, cosmetics and human nutrition. Among different techniques for analysis of EOs, gas chromatography-mass spectrometry (GC-MS) is the most important one in recent years. However, there are some fundamental problems in GC-MS analysis including baseline drift, spectral background, noise, low S/N (signal to noise) ratio, changes in the peak shapes and co-elution. Multivariate curve resolution (MCR) approaches cope with ongoing... 

This paper focuses on characterization of the components of Iranian rosemary essential oil using gas chromatography-mass spectrometry (GC-MS). Multivariate curve resolution (MCR) approach was used to overcome the problem of background, baseline offset and overlapping/embedded peaks in GC-MS. The analysis of GC-MS data revealed that sixty eight components exist in the rosemary essential oil. However, with the help of MCR this number was extended to ninety nine components with concentrations higher than 0.01%, which accounts for 98.23% of the total relative content of the rosemary essential oil. The most important constituents of the Iranian rosemary are 1,8-cineole (23.47%), α-pinene... 

In analysis of complex natural matrices by gas chromatography-mass spectrometry (GC-MS), many disturbing factors such as baseline drift, spectral background, homoscedastic and heteroscedastic noise, peak shape deformation (non-Gaussian peaks), low S/N ratio and co-elution (overlapped and/or embedded peaks) lead the researchers to handle them to serve time, money and experimental efforts. This study aimed to improve the GC-MS analysis of complex natural matrices utilizing multivariate curve resolution (MCR) methods. In addition, to assess the peak purity of the two-dimensional data, a method called variable size moving window-evolving factor analysis (VSMW-EFA) is introduced and examined. The... 

In the present research, ultrasonic-assisted emulsification-microextraction (USAEME) coupled with gas chromatography-mass spectrometry (GC-MS) has been proposed for analysis of thirteen environmental protection agency (EPA) polycyclic aromatic hydrocarbons (PAHs) in aqueous samples. Tetrachloroethylene was selected as extraction solvent. The main parameters of USAEME affecting the efficiency of the method were modeled and optimized using a central composite design (CCD). Under the optimum conditions (9 μL for extraction solvent, 1.15% (w/v) NaCl (salt concentration) and 10min for ultrasonication time), preconcentration factor (PF) of the PAHs was in the range of 500-950. In order to have a... 

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known... 

Independent Component Analysis (ICA) can be used to decompose functional Magnetic Resonance Imaging (fMRI) data into a set of statistically independent images which are likely to be the sources of fMRI data. After applying ICA, a set of independent components are produced, and then, a "meaningful" subset from these components must be identified, because a large majority of components are non-interesting. So, interpreting the components is an important and also difficult task. In this paper, we propose a criterion based on the entropy of time courses to automatically select the components of interest. This method does not require to know the stimulus pattern of the experiment  

This paper presents a study of the Taguchi design method to optimise the rate of electrophoretic deposition (EPD) of Bioglass® particles from aqueous suspensions. The effect of Bioglass® concentration, pH and electric field was investigated. An orthogonal array of L16 type with mixed levels of the control factors was utilized. Multivariate analysis of variance (MANOVA) and regression analysis based on the partial least-square method were used to identify the significant factors affecting the deposition rate and its stability during constant-voltage EPD. It was found that the pH of the suspension significantly influences the deposition rate whereas the applied electric field has the smallest... 

Mean field independent component analysis (MF-ICA) along with other chemometric techniques was proposed for obtaining more information from multi-component gas chromatographic-mass spectrometric (GC-MS) signals of essential oils (mandarin and lemon as examples). Using these techniques, some fundamental problems during the GC-MS analysis of essential oils such as varying baseline, presence of different types of noise and co-elution have been solved. The parameters affecting MF-ICA algorithm were screened using a 25 factorial design. The optimum conditions for MF-ICA algorithm were followed by deconvolution of complex GC-MS peak clusters. The number of independent components (ICs) (chemical...