Sharif Digital Repository

Sample preparation for non-target analysis is challenging due to the difficulty in the extraction of polar and non-polar analytes simultaneously. Most commercial solid sorbents lack the proper comprehensiveness for extraction of analytes with different physiochemical properties. A possible key is the combination of hydrophobic polymer and hydrophilic surface functional groups in solid based extraction methods in order to generate the susceptibility for retaining both polar and non-polar analytes. To pursue this goal, in this study, four polar groups including [sbnd]NH 2 , [sbnd]NO 2 , [sbnd]COOH, and [sbnd]COCH 3 were chemically bound to Amberlite XAD-4 substrate in order to prepare a... 

Development of a convenient and inexpensive method for identification and detection of nanoparticles (NPs) is of great interest. In this work, we have developed a novel and simple chemiluminescence based sensor array, with its sensing mechanism mimicking that of olfactory and gustatory systems for discriminating a set of NPs. The proposed method is based on the enhancement effect of NPs on luminol–oxidant CL intensity by their catalytic effect. Three kinds of oxidant including H2O2, AgNO3, and K3Fe(CN)6 were used as sensor elements and NPs exhibited diverse enhancing responses to different oxidant-luminol CL systems producing unique response patterns that were identified through heat map and... 

Searching large image databases is a time consuming process when done manually. Current CBIR methods mostly rely on training data in specific domains. When source and domain of images are unknown, unsupervised methods provide better solutions. In this work, we use a hierarchical clustering scheme to group images in an unknown and large image database. In addition, the user should provide the current class assignment of a small number of images as a feedback to the system. The proposed method uses this feedback to guess the number of required clusters, and optimizes the weight vector in an iterative manner. In each step, after modification of the weight vector, the images are reclustered. We... 

We propose a novel method for the clustering of point-cloud data that originate from single-molecule localization microscopy (SMLM). Our scheme has the ability to infer a hierarchical structure from the data. It takes a particular relevance when quantitatively analyzing the biological particles of interest at different scales. It assumes a prior neither on the shape of particles nor on the background noise. Our multiscale clustering pipeline is built upon graph theory. At each scale, we first construct a weighted graph that represents the SMLM data. Next, we find clusters using spectral clustering. We then use the output of this clustering algorithm to build the graph in the next scale; in... 

Fuzzy C-means clustering (FCM) is proposed as a promising method for the clustering of chromatographic fingerprints of complex samples, such as essential oils. As an example, secondary metabolites of 14 citrus leaves samples are extracted and analyzed by gas chromatography-mass spectrometry (GC-MS). The obtained chromatographic fingerprints are divided to desired number of chromatographic regions. Owing to the fact that chromatographic problems, such as elution time shift and peak overlap can significantly affect the clustering results, therefore, each chromatographic region is analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to address these problems. Then,... 

The main question of this article is about whether cryptocurrencies, within their decentralization aspects, are a real commodity or/and a virtual currency. To resolve such a dilemma, we compare 7 cryptocurrencies with a sample of the three types of monetary systems: 28 fiat money, 2 commodities, 2 commodity based indices, and 3 financial market indices. We use the matrix correlation method. We display dendrograms and observe “hierarchy clustering”, as a function of data coarse graining. In fact, we confirm that the cryptocurrencies are not decentralized. We observe also that most of the currencies in the world are not significantly correlated or present a weak correlation with... 

This paper presents an online portfolio selection algorithm based on pattern matching principle where it makes a decision on the optimal portfolio in each period and updates the optimal portfolio at the beginning of each period. The proposed method consists of two steps: i) sample selection, ii) portfolio optimization. First, in the sample selection, clustering algorithms including k-means, k-medoids, spectral and hierarchical clustering are applied to discover time windows (TW) similar to the recent time window. Then, after finding the similar time windows and predicting the market behavior of the next day, the optimum function along with the transaction cost is used in the portfolio... 

Due to the widespread use of pesticides and their harmful effects on humans and wildlife, monitoring their residual amounts in crops is critically essential but still challenging regarding the development of high-throughput approaches. Herein, a colorimetric sensor array has been proposed for discrimination and identification of triazole fungicides using monometallic and bimetallic silver and gold nanoparticles. Aggregation-induced behavior of AgNPs, AuNPs, and Au-AgNPs in the presence of four triazole fungicides produced a fingerprint response pattern for each analyte. Innovative changes to the metal composition of nanoparticles leads to the production of entirely distinct response patterns... 

Due to the widespread use of pesticides and their harmful effects on humans and wildlife, monitoring their residual amounts in crops is critically essential but still challenging regarding the development of high-throughput approaches. Herein, a colorimetric sensor array has been proposed for discrimination and identification of triazole fungicides using monometallic and bimetallic silver and gold nanoparticles. Aggregation-induced behavior of AgNPs, AuNPs, and Au-AgNPs in the presence of four triazole fungicides produced a fingerprint response pattern for each analyte. Innovative changes to the metal composition of nanoparticles leads to the production of entirely distinct response patterns... 

Supplier selection is a decision-making process to identify and evaluate suppliers for making contracts. Here, we use interval type-2 fuzzy values to show the decision makers' preferences and also introduce a new formula to compute the distance between two interval type-2 fuzzy sets. The performance of the proposed distance formula in comparison with the normalized Hamming, normalized Hamming based on the Hausdorff metric, normalized Euclidean and the signed distances is evaluated. The results show that the signed distance has the same trend as our method, but the other three methods are not appropriate for interval type-2 fuzzy sets. Using this approach, we propose a hierarchical... 

In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were... 

Herein, some (modified) paper–based substrates were prepared and utilized as extractive phases in a microfluidic device and their extraction performances examined for analytes with different polarities. Reagents including hexadecyltrimethoxysilane (HDTMS), phenyltrimethoxysilane (PTES), (3-aminopropyl) triethoxysilane (APTES) and 3–(2,3–epoxypropoxy) propyltrimethoxysilane (EPPTMOS) were implemented for the modification process. Due to the induction of different silane functional groups, it was anticipated to have various interactions for the tested analytes. Eventually, the prepared paper sheets were used as extractive phases for solid–phase extraction within a microfluidic system. The... 

Clustering Web data is one important technique for extracting knowledge from the Web. In this paper, a novel method is presented to facilitate the clustering. The method determines the appropriate number of clusters and provides suitable representatives for each cluster by inference from a Bayesian network. Furthermore, by means of the Bayesian network, the contents of the Web pages are converted into vectors of lower dimensions. The method is also extended for hierarchical clustering, and a useful heuristic is developed to select a good hierarchy. The experimental results show that the clusters produced benefit from high quality  

World Wide Web is a huge information space, making it a valuable resource for decision making. However, it should be effectively managed for such a purpose. One important management technique is clustering the web data. In this paper, we propose some developments in clustering methods to achieve higher qualities. At first we study a new density based method adapted for hierarchical clustering of web documents. Then utilizing the hyperlink structure of web, we propose a new method that incorporates density concepts with web graph. These algorithms have the preference of low complexity and as experimental results reveal, the resultant clusters have high quality. © 2009 Elsevier B.V. All rights... 

The rapid increase of information on the web makes it necessary to improve information management techniques. One of the most important techniques is clustering web data. In this paper, we propose a new 3-phase clustering method that finds dense units in a data set using density-based algorithms. The distances in the dense units are stored in order in structures such as a min heap. In the extraction stage, these distances are extracted one by one, and their effects on the clustering process are examined. Finally, in the combination stage, clustering is completed using improved versions of well-known single and average linkage methods. All steps of the methods are performed in O(n log n) time...