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Search Results: 1 - 10 of 90 matches for " Mehrnoosh Shaghaghi "
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Effect of endurance training and cinnamon supplementation on post-exercise oxidative responses in rats
Gholamreza Dehghan, Mehrnoosh Shaghaghi, Afshar Jafari, Mustafa Mohammadi, Reza Badalzadeh
Molecular Biology Research Communications , 2014,
Abstract: Despite the preventative and therapeutic effects of regular exercise, exhaustive exercise may be harmful to health. The present study aimed to determine the protective effect of endurance training and cinnamon bark extract (CBE) supplementation on oxidative responses induced by an exhaustive exercise schedule in rats. The rats were randomly divided into the following five groups of 6; control sedentary (Con/Sed), control exercised (Con/Ex), trained exercised (Tr/Ex), supplemented exercised (Sup/Ex), and trained, supplemented and exercised (Tr/Sup/Ex). Animals in exercise groups ran on a rodent treadmill for an 8-week endurance training program. At the end of the experiment, blood samples were collected and (MDA) and total thiol (TT) levels were measured in plasma. Glutathione peroxidase (GPX), superoxide dismutase (SOD), and catalase (CAT) activities were determined in soleus muscles. Results showed significant increases in SOD activity and malondealdehyde (MDA) levels in the soleus muscles and serum of exercised rats fed with the normal diet. The exhaustive exercise also induced a decrease in serum total thiol level and GPX activity. Elevated levels of total thiol and total antioxidant capacity (TAC) and reduced serum MDA levels were found in the Sup/Ex and Tr/Sup/Ex groups. CAT and GPX activities increased by CBE treatment in trained rats. Regular training increased CAT and GPX activities in the Tr/Sup/Ex group. CAT, GPX and SOD activities were not affected by the CBE treatment in untrained rats. Results suggest that additional use of regular training and CBE supplementation increase TAC and protect healthy male rats against oxidative damage induced by exhaustive exercise.
Hyperspectral Image Classification Based on Hierarchical SVM Algorithm for Improving Overall Accuracy  [PDF]
Lida Hosseini, Ramin Shaghaghi Kandovan
Advances in Remote Sensing (ARS) , 2017, DOI: 10.4236/ars.2017.61005
Abstract:
One of the most challenges in the remote sensing applications is Hyperspectral image classification. Hyperspectral image classification accuracy depends on the number of classes, training samples and features space dimension. The classification performance degrades to increase the number of classes and reduce the number of training samples. The increase in the number of feature follows a considerable rise in data redundancy and computational complexity leads to the classification accuracy confusion. In order to deal with the Hughes phenomenon and using hyperspectral image data, a hierarchical algorithm based on SVM is proposed in this paper. In the proposed hierarchical algorithm, classification is accomplished in two levels. Firstly, the clusters included similar classes is defined according to Euclidean distance between the class centers. The SVM algorithm is accomplished on clusters with selected features. In next step, classes in every cluster are discriminated based on SVM algorithm and the fewer features. The features are selected based on correlation criteria between the classes, determined in every level, and features. The numerical results show that the accuracy classification is improved using the proposed Hierarchical SVM rather than SVM. The number of bands used for classification was reduced to 50, while the classification accuracy increased from 73% to 80% with applying the conventional SVM and the proposed Hierarchical SVM algorithm, respectively.
We Need More Focus On Pre-Disaster Preparedness: Early Lessons Learned From Recent Earthquakes in Northwest of Iran
Abdolreza Shaghaghi
Health Promotion Perspectives , 2012, DOI: 10.5681/hpp.2012.035
Abstract: Dear Editor-in-ChiefTwo strong earthquakes with the magnitude of 6.4 and 6.3 at a depth of 9.9 km that rattled Iran’s northwest region within 60 km of Tabriz, the capital city of East Azerbaijan province on August 11, 2012 caused extensive damage in about 1000 villages, killed at least 258 and injured 1380 people. The quakes most severely affected villages close to three impacted towns in the disaster area; Varzegan, Ahar and Heris. Some of the villages were hit are in remote areas with limited access to transport routes.Within early hours aftermath of the twin devastating incidents ordinary people and those who had relatives in the affected area rushed towards the region to salvage victims mainly by their own cars. Independent groups such as small units from armed forces were also sent to the region to support rescue operation. Some of the survivors meanwhile, tried to transfer severely injured survivors to nearby hospitals and even to the central hospital in Tabriz using public transportation facilities e.g. taxis, vans or any vehicle available at the time. All these unplanned efforts created traffic jams on the roads leading to the disaster area and delayed rescue operation by trained staff.Now after the earthquakes that rumbled through the disaster area, about 36,000 quake-stricken people were given shelter and are being provided with their basic needs in makeshift camps. Humanitarian aids are reaching affected zone from all over the country and internationally but there are inadequacies in proper distribution of food stuff and equipments among the survivors. Piles of water bottles in front of tents which left in the heat under the sunshine, clothes and canned foods which were distributed by volunteers and are more than current needs of the victims are observable in the disaster area. This is while, lack of sufficient supply of drinking water, canned foods and portable washrooms were reported by the authorities in the first days after the quake.Iran is located on major earthquake fault line and occurrence of stronger quakes with more destructive consequences is probable in future. The scale of destruction and number of victims both those who survived without major injuries and those who severely injured or passed away as a result of recent quakes was not very much beyond the national and even the local disaster relief capacity.Lack of a complete coordination during rescue and also relief operation is indicating pitfalls and inadequacies we suffer in our pre-disaster phase of preparedness programmes nationally and locally. Prior harmonizing of relief
Ockham's razor and reasoning about information flow
Mehrnoosh Sadrzadeh
Mathematics , 2008,
Abstract: What is the minimal algebraic structure to reason about information flow? Do we really need the full power of Boolean algebras with co-closure and de Morgan dual operators? How much can we weaken and still be able to reason about multi-agent scenarios in a tidy compositional way? This paper provides some answers.
Investigating Efficacy of “Working Memory Training Software” on Students Working Memory  [PDF]
Mehrnoosh Fahimi, Ali Akbar Arjmandnia, Jalil Fathabadi
Health (Health) , 2014, DOI: 10.4236/health.2014.616259
Abstract: The aim of the present study is developing “Working Memory Training Software”, and investigating its content validity and the efficacy of this computerized cognitive training on students working memory. This study is in R & D research category, and it is performed in a semi-experimental design. Its data were collected from students of the third grade (30), fourth grade (30), and fifth grade (12) of primary school. After specifying the software content validity by asking expertise opinions, and investigating these opinions through Spearman Test (rs = 1), these children attended in intervention program for 10 sessions. The subtests of working memory in “Tehran-Stanford-Binet Intelligence Scale” and “Wechsler intelligence scale for children” were conducted in all three groups on the pre-test and post-test. After elimination of the pre-test effect, Paired-Samples T-Test on total scores of subtests of working memory in “Tehran-Stanford Binet Intelligence Scale” (t = 10.869, df = 71, r = 0.967 & P < 0.05) and in “Wechsler intelligence scale for children” (t = 16.809, df = 71, r = 0.983 & P < 0.05) reveals a significant difference in post-test scores. Based on this study the Working Memory Training Software has proper psychometric properties and causes significant improvement in students working memory performance.
Spectral Estimation from Undersampled Data: Correlogram and Model-Based Least Squares
Mahdi Shaghaghi,Sergiy A. Vorobyov
Mathematics , 2012,
Abstract: This paper studies two spectrum estimation methods for the case that the samples are obtained at a rate lower than the Nyquist rate. The first method is the correlogram method for undersampled data. The algorithm partitions the spectrum into a number of segments and estimates the average power within each spectral segment. We derive the bias and the variance of the spectrum estimator, and show that there is a tradeoff between the accuracy of the estimation and the frequency resolution. The asymptotic behavior of the estimator is also investigated, and it is proved that this spectrum estimator is consistent. A new algorithm for reconstructing signals with sparse spectrum from noisy compressive measurements is also introduced. Such model-based algorithm takes the signal structure into account for estimating the unknown parameters which are the frequencies and the amplitudes of linearly combined sinusoidal signals. A high-resolution spectral estimation method is used to recover the frequencies of the signal elements, while the amplitudes of the signal components are estimated by minimizing the squared norm of the compressed estimation error using the least squares technique. The Cramer-Rao bound for the given system model is also derived. It is shown that the proposed algorithm approaches the bound at high signal to noise ratios.
Finite-Length and Asymptotic Analysis of Correlogram for Undersampled Data
Mahdi Shaghaghi,Sergiy A. Vorobyov
Computer Science , 2013,
Abstract: This paper studies a spectrum estimation method for the case that the samples are obtained at a rate lower than the Nyquist rate. The method is referred to as the correlogram for undersampled data. The algorithm partitions the spectrum into a number of segments and estimates the average power within each spectral segment. This method is able to estimate the power spectrum density of a signal from undersampled data without essentially requiring the signal to be sparse. We derive the bias and the variance of the spectrum estimator, and show that there is a tradeoff between the accuracy of the estimation, the frequency resolution, and the complexity of the estimator. A closed-form approximation of the estimation variance is also derived, which clearly shows how the variance is related to different parameters. The asymptotic behavior of the estimator is also investigated, and it is proved that this spectrum estimator is consistent. Moreover, the estimation made for different spectral segments becomes uncorrelated as the signal length tends to infinity. Finally, numerical examples and simulation results are provided, which approve the theoretical conclusions.
Subspace Leakage Analysis and Improved DOA Estimation with Small Sample Size
Mahdi Shaghaghi,Sergiy A. Vorobyov
Statistics , 2015,
Abstract: Classical methods of DOA estimation such as the MUSIC algorithm are based on estimating the signal and noise subspaces from the sample covariance matrix. For a small number of samples, such methods are exposed to performance breakdown, as the sample covariance matrix can largely deviate from the true covariance matrix. In this paper, the problem of DOA estimation performance breakdown is investigated. We consider the structure of the sample covariance matrix and the dynamics of the root-MUSIC algorithm. The performance breakdown in the threshold region is associated with the subspace leakage where some portion of the true signal subspace resides in the estimated noise subspace. In this paper, the subspace leakage is theoretically derived. We also propose a two-step method which improves the performance by modifying the sample covariance matrix such that the amount of the subspace leakage is reduced. Furthermore, we introduce a phenomenon named as root-swap which occurs in the root-MUSIC algorithm in the low sample size region and degrades the performance of the DOA estimation. A new method is then proposed to alleviate this problem. Numerical examples and simulation results are given for uncorrelated and correlated sources to illustrate the improvement achieved by the proposed methods. Moreover, the proposed algorithms are combined with the pseudo-noise resampling method to further improve the performance.
Cramer-Rao Bound for Sparse Signals Fitting the Low-Rank Model with Small Number of Parameters
Mahdi Shaghaghi,Sergiy A. Vorobyov
Statistics , 2015,
Abstract: In this paper, we consider signals with a low-rank covariance matrix which reside in a low-dimensional subspace and can be written in terms of a finite (small) number of parameters. Although such signals do not necessarily have a sparse representation in a finite basis, they possess a sparse structure which makes it possible to recover the signal from compressed measurements. We study the statistical performance bound for parameter estimation in the low-rank signal model from compressed measurements. Specifically, we derive the Cramer-Rao bound (CRB) for a generic low-rank model and we show that the number of compressed samples needs to be larger than the number of sources for the existence of an unbiased estimator with finite estimation variance. We further consider the applications to direction-of-arrival (DOA) and spectral estimation which fit into the low-rank signal model. We also investigate the effect of compression on the CRB by considering numerical examples of the DOA estimation scenario, and show how the CRB increases by increasing the compression or equivalently reducing the number of compressed samples.
Student evaluation of the academic advising process in an Iranian medical school
Azra Shamsdin,Mehrnoosh Doroudchi
International Journal of Medical Education , 2012, DOI: 10.5116/ijme.4f29.a809
Abstract: Objective: The purpose of this study was to examine student evaluation of the academic advising process in an Iranian medical school. Method: We conducted a cross sectional survey of all fourth and fifth year students who studied medicine, nursing and laboratory technology. A short version of a validated questionnaire was administrated to 85 students (23 males and 62 females) at Fasa Medical School, Iran. Results: Of the students, 48 (56 were satisfied with the academic advising process. The descriptive analysis of the study showed that many students (n=72) valued the importance of feedback on student ability in the academic advising process. A further descriptive analysis showed that 34 students (40 were satisfied that advisers were aware of their records. There was a significant difference between student's main course (χ[sup]2[/sup][sub](2)[/sub] = 8.9; p = 0.012) and satisfaction with academic advising. However, the observed differences between female and male students in this study were not statistically significant (χ[sup]2[/sup][sub](1)[/sub] = 2.2; p= 0.107). Conclusions: The results of this study reveal a lack of systematic planning, skills and resources for the academic advising process at the Fars Medical School. The results indicate the need for academic staff development initiatives to improve the academic advising process. An ongoing evaluation program of the academic needs of students may help to advisors to provide academic advising and academic support for students in various courses.
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