Kharazmi University

About: Kharazmi University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Membrane & Supply chain. The organization has 3395 authors who have published 5321 publications receiving 45645 citations. The organization is also known as: Tarbiat Moallem University of Tehran & Teacher Training University.

Effect of Social Media on Child Obesity: Application of Structural Equation Modeling with the Taguchi Method

Abstract: Through public health studies, specifically on child obesity modeling, research scholars have been attempting to identify the factors affecting obesity using suitable statistical techniques. In recent years, regression, structural equation modeling (SEM) and partial least squares (PLS) regression have been the most widely employed statistical modeling techniques in public health studies. The main objective of this study to apply the Taguchi method to introduce a new pattern rather than a model for analyzing the body mass index (BMI) of children as a representative of childhood obesity levels mainly related to social media use. The data analysis includes two main parts. The first part entails selecting significant indicators for the proposed framework by applying SEM for primary and high school students separately. The second part introduces the Taguchi method as a realistic and reliable approach to exploring which combination of significant variables leads to high obesity levels in children. AMOS software (IBM, Armonk, NY, USA) was applied in the first part of data analysis and MINITAB software (Minitab Inc., State College, PA, USA) was utilized for the Taguchi experimental analysis (second data analysis part). This study will help research scholars view the data and a pattern rather than a model, as a combination of different factor levels for target factor optimization.

A highly sensitive miR-195 nanobiosensor for early detection of Parkinson’s disease

Abstract: Early detection of Parkinson's disease (PD), as a dangerous neurodegenerative disease, is a key factor in the therapy or prevention of further development of this disease. We developed an electrochemical nanobiosensor for early detection of PD based on the quantification of circulating biomarker, miR-195. Exfoliated graphene oxide (EGO) and gold nanowires (GNWs) were used to modify the surface of screen-printed carbon electrode. A single-strand thiolated probe was designed for specific hybridization with target miRNA (miR-195), and doxorubicin was used as an electrochemical indicator for differential pulse voltammetry measurements. The results of scanning electron microscope imaging and cyclic voltammetry experiments confirmed the accuracy of the working electrode modification steps. The results of analytical performance nanobiosensor showed a high sensitivity of the biosensing with 2.9 femtomolar detection limit and dynamic range of 10.0-900.0 femtomolar. In addition, good selectivity for target miRNA over non-specific oligonucleotides (one and three base replacement in target miRNA, and non-complementary) was achieved. The results of real human serum analysis did not show any interference in the function of the biosensor. Based on the results, the miR-195 electrochemical nanobiosensor could be suggested for clinicians in the medical diagnosis of PD.

The greater effect of high-intensity interval training versus moderate-intensity continuous training on cardioprotection against ischemia-reperfusion injury through Klotho levels and attenuate of myocardial TRPC6 expression

Abstract: Myocardial ischemia-reperfusion (IR) injury is a leading cause of death all over the world, so developing practical approaches to promote cardioprotection against IR injury is essential. Exercise training is an effective strategy to improve cardioprotection. Hence, the purpose of this study was to investigate the effect of short-term preconditioning with two types of high-intensity interval training (HIIT) and moderate intensity continuous training (MICT) on klotho and TRPC6 mechanisms in cardioprotection. Eighty Male Wistar rats (250–300 g) were randomly divided into 7 groups, including Control, HIIT, MICT, Sham, IR, HIIT+IR, and MICT+IR. Training was performed in 5 consecutive days. HIIT protocol consisted of running on the treadmill at intervals 85–90% vo2max that separated by slow intensity periods at 50–60% vo2max. MICT program was performed at 70% VO2max at the same running distance with HIIT groups. The cardiac IR injury was induced by LAD occlusion followed by reperfusion. ELISA kit was used in order to measure the plasma levels of klotho, LDH and CK-MB, and TRPC6 expression was determined using the western blot technique. Data were analyzed using one way ANOVA and Tukey’s post hoc tests. The results of this study showed that both types of exercise training programs significantly increase plasma levels of klotho and reduce the infarct size and heart injury. In addition, the exercise training decreased the amount of TRPC6 channels expression during IR. However, the effect of HIIT on increasing the klotho and cardioprotection was greater compared to MICT. Based on the results, even a short-term of aerobic exercise training, especially HIIT, promotes cardioprotection against IR injury and decreases infarct size via an increase in klotho and attenuate of protein expression of myocardial TRPC6 during IR.

Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines

Abstract: Two novel methods are presented to detect short-circuit fault in transmission lines.Comparing a soft computing based method with a hardware based method.Two methods have higher accuracy and shorter process time compared to others.Filter based method has an accuracy of 100% in presence of 10% disturbance.DWT based method has an accuracy of 97% in presence of 10% disturbance. In this study, two approaches are presented to detect short-circuit faults in power transmission lines. The two proposed methods are completely novel from both theoretical and technical aspects. The first approach is a soft computing method that uses discrete wavelet transform with Daubechies mother wavelets db1, db2, db3, and db4. The second approach is a hardware based method that utilizes a novel proposed two-stage finite impulse response filter with a sampling frequency of 32kHz, and a very short process time about three samples time. The two approaches are analyzed by presenting theoretical results. Simulated results obtained by simulating a three-phase 230kV, 50Hz power transmission line are given that validate the theoretical results, and explicitly verify that the filter based approach has an accuracy of 100% in presence of 10% disturbance while the accuracy of the wavelet transform based approach is maximally 97%, but it has less complication and implementation cost. Another comparative study between this work and other works shows that the two proposed methods have higher accuracy and very shorter process time compared to the other methods, especially in presence of 10% disturbance that actually occurs in power transmission lines.