Sharif Digital Repository

Dental professionals are in a unique position to promote smoking cessation among their patients. We evaluated the effects of a brief counselling intervention by a dentist on patients' attitude towards the role of dentists in tobacco cessation programmes. In a semi-experimental study in Tehran, Islamic Republic of Iran, 70 eligible smokers were selected and randomly assigned to intervention and control groups. The initial attitudes of the patients regarding tobacco cessation counselling services provided by the dentist were determined using a validated questionnaire. The intervention group received a brief chair-side counselling by a dentist based on the 5 A's approach, while no intervention... 

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic... 

Smoking is known as a major cause of chronic obstructive pulmonary disease (COPD) and hence immediate and effective interventions are required for its elimination. This study aimed to collect valid data with regard to cigarette smoking in adult population of north of Iran for policy making by a meta-analysis of the documents of national non-communicable disease risk factors surveillance system. We investigated relevant evidences by searching in published and non-electronic databases. Data were extracted based on variables such as year of the study, sex, age group and prevalence of smoking habit. Based on results of heterogeneity, we applied fixed or random effects model to estimate the... 

A new approach is introduced to estimate the formal information of neurons. Formal Information, mainly discusses about the aspects of the response that is related to the stimulus. Estimation is based on introducing a mathematical nonlinear model with Hammerstein-Wiener system estimator. This method of system identification consists of three blocks to completely describe the nonlinearity of input and output and linear behaviour of the model. The introduced model is trained by 166 spikes of neurons and other 166 spikes are used to test and validate the model. The simulation results show the R-Value of 92.6 % between estimated and reference information rate. This shows improvement of 1.41 % in... 

The impact of adding correlation to a population of neurons on the information and the activity of the population is one of the fundamental questions in recent system neuroscience. In this paper, we would like to introduce topology-based correlation at the level of storing patterns in a recurrent network. We then study the effects of topological patterns on the activity and memory capacity of the network. The general aim of the present work is to show how the repertoire of possible stored patterns is determined by the underlying network topology.Two topological probability rules for pattern selection in recurrent network are introduced. The first one selects patterns according to a... 

Constrained clustering methods (that usually use must-link and/or cannot-link constraints) have been received much attention in the last decade. Recently, kernel adaptation or kernel learning has been considered as a powerful approach for constrained clustering. However, these methods usually either allow only special forms of kernels or learn non-parametric kernel matrices and scale very poorly. Therefore, they either learn a metric that has low flexibility or are applicable only on small data sets due to their high computational complexity. In this paper, we propose a more efficient non-linear metric learning method that learns a low-rank kernel matrix from must-link and cannot-link... 

Purpose - The purpose of this paper is to describe optical flow-based navigation of a very light fixed-wing aircraft in flight between obstacles. Design/methodology/approach - The optical flow information of two cameras mounted on the aircraft is used to detect the obstacle. It is assumed that the image processing has been completed and the optical flow vectors have been obtained beforehand. The optical flow is used to detect the obstacles and make a rapid turn manoeuvre for the aircraft. Findings - It is shown that using the optical flow feedback by itself is unable to give a rapid turn to the aircraft and its rate should be employed into the control law. Six degree-of-freedom flight... 

Knowledge architecture (KA) establishes the basic groundwork for the successful implementation of a short-term or long-term knowledge management (KM) program. An example of KA is the design of a prototype before a new vehicle is manufactured. Due to a transformation to large-scale organizations, the traditional architecture of organizations is undergoing fundamental changes. This paper explores the main strengths and weaknesses in the field of KA within large-scale organizations and provides a suitable methodology and supervising framework to overcome specific limitations. This objective was achieved by applying and updating the concepts from the Zachman information architectural framework... 

Data stream classification is one of the most challenging areas in the machine learning. In this paper, we focus on three major challenges namely infinite length, concept-drift and concept-evolution. Infinite length causes the inability to store all instances. Concept-drift is the change in the underlying concept and occurs in almost every data stream. Concept-evolution, in fact, is the arrival of novel classes and is an undeniable phenomenon in most real world data streams. There are lots of researches about data stream classification, but most of them focus on the first two challenges and ignore the last one. In this paper, we propose new method based on ensembles whose classifiers use... 

Today, public awareness about the benefits of using ontologies in information processing and the semantic web has increased. Since ontologies are useful in various applications, many large ontologies have been developed so far. But various areas like publication, maintenance, validation, processing, and security policies need further research. One way to better tackle these areas is to partition large ontologies into sub partitions. In this paper, we present a new method to partition large ontologies. For the proposed method, three steps are required: (1) transforming an ontology to a weighted graph, (2) partitioning the graph with an algorithm which recognizes the most important concepts,... 

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... 

Data related to force-deformation behaviour of soft tissue plays an important role in medical/surgical applications such as realistically modelling mechanical behaviour of soft tissue as well as minimally invasive surgery (MIS) and medical diagnosis. While the mechanical behaviour of soft tissue is very complex due to its different constitutive components, some issues increase its complexity like behavioural changes between the live and dead tissues. Indeed, an adequate quantitative description of mechanical behaviour of soft tissues requires high quality in vivo experimental data to be obtained and analysed. This paper describes a novel laparoscopic grasper with two parallel jaws capable of... 

Background and Objectives: Melanoma is a dangerous kind of skin disease with a high death rate, and its prevalence has increased rapidly in recent years. Diagnosis of melanoma in a primary phase can be helpful for its cure. Due to costs for dermatology, we need an automatic system to diagnose melanoma through lesion images. Methods: Here, we propose a sparse representation based method for segmentation and classification of lesion images. The main idea of our framework is based on a kernel sparse representation, which produces discriminative sparse codes to represent features in a high-dimensional feature space. Our novel formulation for discriminative kernel sparse coding jointly learns a... 

Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features’ dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound’s mechanism of action (MoA) and a gene’s pathway. © 2019, The Author(s)  

This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms... 

Background and objective: Steady-state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) are useful methods in the brain-computer interface (BCI) systems. Hybrid BCI systems that combine these two approaches can enhance the proficiency of the P300 spellers. Methods: In this study, a new hybrid RSVP/SSVEP BCI is proposed to increase the classification accuracy and information transfer rate (ITR) as compared with the other RSVP speller paradigms. In this paradigm, RSVP (eliciting a P300 response) and SSVEP stimulations are presented in such a way that the target group of characters is identified by RSVP stimuli, and the target character is recognized by SSVEP stimuli.... 

In this study, optical technology is considered as SA issues’ solution with the potential ability to increase the speed, overcome memory-limitation, reduce power consumption, and increase output accuracy. So we examine the effect of bio-data encoding and the creation of input images on the pattern-recognition error-rate at the output of optical Vander-lugt correlator. Moreover, we present a genetic algorithm-based coding approach, named as GAC, to minimize output noises of cross-correlating data. As a case study, we adopt the proposed coding approach within a correlation-based optical architecture for counting k-mers in a DNA string. As verified by the simulations on Salmonella whole-genome,... 

In this study, optical technology is considered as SA issues’ solution with the potential ability to increase the speed, overcome memory-limitation, reduce power consumption, and increase output accuracy. So we examine the effect of bio-data encoding and the creation of input images on the pattern-recognition error-rate at the output of optical Vander-lugt correlator. Moreover, we present a genetic algorithm-based coding approach, named as GAC, to minimize output noises of cross-correlating data. As a case study, we adopt the proposed coding approach within a correlation-based optical architecture for counting k-mers in a DNA string. As verified by the simulations on Salmonella whole-genome,... 

Common Spatial Pattern (CSP) is a powerful feature extraction method in brain-computer interface (BCI) systems. However, the CSP method has some deficiencies that limit its beneficiary. First, this method is not useful when data is noisy, and it is necessary to have a large dataset because CSP is inclined to overfit. Second, the CSP method uses just the spatial information of the data, and it cannot incorporate the temporal and spectral information. In this paper, we propose a new CSP-based algorithm which is capable of employing the information in all dimensions of data. Also, by defining the regularization term for each mode of information, we can diminish the noise effects and overfitting...