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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.
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.
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.
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>