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- 저자 : Movaghar, Ali (검색결과 9 건)
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Epidemiology of Primary CNS Tumors in Iran: A Systematic Review
Jazayeri, Seyed Behzad,Rahimi-Movaghar, Vafa,Shokraneh, Farhad,Saadat, Soheil,Ramezani, Rashid Asian Pacific Journal of Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.6
Background: Although primary malignant CNS tumors are registered in the national cancer registry (NCR) of Iran, there are no available data on the incidence of the primary malignant or benign CNS tumors and their common histopathologies in the country. This study analyzed the 10-year data of the Iranian NCR from March 21, 2000 to March 20, 2010, including a systematic review. Materials and Methods: The international and national scientific databases were searched using the search keywords CNS, tumor, malignancy, brain, spine, neoplasm and Iran. Results: Of the 1,086 primary results, 9 papers were selected and reviewed, along with analysis of 10-year NCR data. The results showed that primary malignant brain tumors have an overall incidence of 2.74 per 100,000 person-years. The analysis of the papers revealed a benign to malignant ratio of 1.07. The most common histopathologies are meningioma, astrocytoma, glioblastoma and ependymoma. These tumors are more common in men (M/F=1.48). Primary malignant spinal cord tumors constitute 7.1% of the primary malignant CNS tumors with incidence of 0.21/100,000. Conclusions: This study shows that CNS tumors in Iran are in compliance with the pattern of CNS tumors in developing countries. The NCR must include benign lesions to understand the definitive epidemiology of primary CNS tumors in Iran.
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Kazem Khalagi,Mohammad Ali Mansournia,Afarin Rahimi-Movaghar,Keramat Nourijelyani,Masoumeh Amin-Esmaeili,Ahmad Hajebi,Vandad Sharifi,Reza Radgoodarzi,Mitra Hefazi,Abbas Motevalian 한국역학회 2016 Epidemiology and Health Vol.38 No.-
Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.
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Improved Region-Based TCTL Model Checking of Time Petri Nets
Esmaili, Mohammad Esmail,Entezari-Maleki, Reza,Movaghar, Ali Korean Institute of Information Scientists and Eng 2015 Journal of Computing Science and Engineering Vol.9 No.1
The most important challenge in the region-based abstraction method as an approach to compute the state space of time Petri Nets (TPNs) for model checking is that the method results in a huge number of regions, causing a state explosion problem. Thus, region-based abstraction methods are not appropriate for use in developing practical tools. To address this limitation, this paper applies a modification to the basic region abstraction method to be used specially for computing the state space of TPN models, so that the number of regions becomes smaller than that of the situations in which the current methods are applied. The proposed approach is based on the special features of TPN that helps us to construct suitable and small region graphs that preserve the time properties of TPN. To achieve this, we use TPN-TCTL as a timed extension of CTL for specifying a subset of properties in TPN models. Then, for model checking TPN-TCTL properties on TPN models, CTL model checking is used on TPN models by translating TPN-TCTL to the equivalent CTL. Finally, we compare our proposed method with the current region-based abstraction methods proposed for TPN models in terms of the size of the resulting region graph.
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Improved Region-Based TCTL Model Checking of Time Petri Nets
Mohammad Esmail Esmaili,Reza Entezari-Maleki,Ali Movaghar 한국정보과학회 2015 Journal of Computing Science and Engineering Vol.9 No.1
The most important challenge in the region-based abstraction method as an approach to compute the state space of time Petri Nets (TPNs) for model checking is that the method results in a huge number of regions, causing a state explosion problem. Thus, region-based abstraction methods are not appropriate for use in developing practical tools. To address this limitation, this paper applies a modification to the basic region abstraction method to be used specially for computing the state space of TPN models, so that the number of regions becomes smaller than that of the situations in which the current methods are applied. The proposed approach is based on the special features of TPN that helps us to construct suitable and small region graphs that preserve the time properties of TPN. To achieve this, we use TPN-TCTL as a timed extension of CTL for specifying a subset of properties in TPN models. Then, for model checking TPN-TCTL properties on TPN models, CTL model checking is used on TPN models by translating TPN-TCTL to the equivalent CTL. Finally, we compare our proposed method with the current region-based abstraction methods proposed for TPN models in terms of the size of the resulting region graph.
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Power-aware performance analysis of self-adaptive resource management in IaaS clouds
Ataie, Ehsan,Entezari-Maleki, Reza,Etesami, Sayed Ehsan,Egger, Bernhard,Ardagna, Danilo,Movaghar, Ali North-Holland 2018 Future generations computer systems Vol.86 No.-
<P><B>Abstract</B></P> <P>In this paper, Stochastic Activity Networks (SANs) are used to model and evaluate the performance and power consumption of an Infrastructure-as-a-Service (IaaS) cloud. The proposed SAN model is scalable and flexible, yet encompasses some details of an IaaS cloud, such as Virtual Machine (VM) provisioning, VM multiplexing, and failure/repair behavior of VMs. Using the proposed SAN, a power-aware self-adaptive resource management scheme is presented for IaaS clouds that automatically adjusts the number of powered-on Physical Machines (PMs) regarding variable workloads in different time intervals. The proposed scheme respects user-oriented metrics by avoiding Service Level Agreement (SLA) violations while taking provider-oriented metrics into consideration. The behavior of the proposed scheme is analyzed when the arriving workload changes, and then its performance is compared with two non-adaptive baselines based on diverse performance and power consumption measures defined on the system. A validation of the proposed SAN model and the resource management scheme against an adapted version of the CloudSim framework is also presented.</P> <P><B>Highlights</B></P> <P> <UL> <LI> An analytical model is proposed for Infrastructure-as-a-Service (IaaS) clouds taking several details of such systems into consideration. </LI> <LI> A self-adaptive power-aware and Service Level Agreement (SLA)-aware resource management scheme is presented for cloud systems. </LI> <LI> The presented scheme adjusts the number of powered-on Physical Machines (PMs) according to the input workload. </LI> <LI> A validation of the proposed model and scheme against the CloudSim framework is presented. </LI> </UL> </P>
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Amirmohammad Toloui,Hamzah Adel Ramawad,Pantea Gharin,Alexander R. Vaccaro,Hamed Zarei,Mostafa Hosseini,Mahmoud Yousefifard,Vafa Rahimi-Movaghar 대한척추신경외과학회 2023 Neurospine Vol.20 No.3
Objective: The objective of this systematic review and meta-analysis was to assess the efficacy of exercise in neuropathic pain following traumatic spinal cord injuries. Methods: The search was conducted in MEDLINE, Embase, Scopus, and Web of Science by the end of 2022. Two independent researchers included the articles based on the inclusion and exclusion criteria. A standardized mean difference was calculated for each data and they were pooled to calculate an overall effect size. To assess the heterogeneity between studies, I2 and chi-square tests were utilized. In the case of heterogeneity, meta-regression was performed to identify the potential source. Results: Fifteen preclinical studies were included. Meta-analysis demonstrated that exercise significantly improves mechanical allodynia (standardized mean difference [SMD], -1.59; 95% confidence interval [CI], -2.16 to -1.02; p < 0.001; I2 = 90.37%), thermal hyperalgesia (SMD, 1.95; 95% CI, 0.96–2.94; p < 0.001), and cold allodynia (SMD, -2.92; 95% CI, -4.4 to -1.43; p < 0.001). The improvement in mechanical allodynia is significantly more in animals with a compression model of SCI (meta-regression coefficient, -1.33; 95% CI, -1.84 to -0.57; p < 0.001) and in mild SCI (p < 0.001). Additionally, the improvement was more prominent if the training was started 7 to 8 days postinjury (coefficient, -2.54; 95% CI, -3.85 to -1.23; p < 0.001) and was continued every day (coefficient, -1.99; 95% CI, -3.07 to -0.9; p < 0.001). Likewise, voluntary exercise demonstrated a significantly more effect size (coefficient, -1.45; 95% CI, -2.67 to -0.23; p = 0.02). Conclusion: Exercise is effective in the amelioration of neuropathic pain. This effect in mechanical allodynia is more prominent if voluntary, continuous training is initiated in the subacute phase of mild SCI.
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( Payman Salamati ),( Reza Rostami ),( Soheil Saadat ),( Taher Taheri ),( Maryam Tajabadi ),( Ghazale Ranjbari ),( Zohrehsadat Naji ),( Saba Jafarpour ),( Vafa Rahimi Movaghar ) 대한내과학회 2014 대한내과학회 추계학술발표논문집 Vol.2014 No.1
Background: Patients with spinal cord injury (SCI) have a lower health related quality of life (HRQOL) in comparison with both healthy controls and the normal population. The aim of this study was to compare HRQOL between two groups of veteran and non-veteran SCI patients. Methods: Male paraplegic non-veterans who had sustained complete SCI before the year 1988 and were residents of Tehran province, Iran, and a similar group of SCI veterans were enrolled in this study. The participants were interviewed based on the Iranian version of SF-36 questionnaire by two psychologists. Eight sub-scales and two physical and mental component summaries of the instrument were assessed. We used chi-square, odds ratio, Mann-Whitney U, independent t-test, and linear regression for analysis. Results: Overall, 25 veterans and 22 non-veterans were enrolled in the study. The mean age, time since injury, and presence of comorbid illnesses were not signifi cantly different between veterans and non-veterans. A greater number of veterans were married (P=0. 003) and employed (P=0. 047). On average, veterans had more years of formal education than non-veterans (P=0. 001). The mean (SD) bodily pain sub-scale was 72. 73(31. 253) for non-veterans and 49. 7(28. 287) for veterans (P=0. 011). Absence of comorbid illnesses was associated with a better physical component summary (P<0. 001). Employment was associated with a better mental component summary (P=0. 022). Conclusions: We did not fi nd any difference in HRQOL between the two groups except for the bodily pain sub-scale. Further studies with larger sample sizes are recommended.
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