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Employing Minimum age model (MAM) and Finite mixture modeling (FMM) for OSL age determination of two important samples from Ira Trench of North Tehran Fault

Fattahi M., Heidary M., Ghasemi M.

Geochronometria

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2016

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Vol. 43, no. 1

38--47

EN

Ira trench site is in a point where, the surface trace of North Tehran Fault (NTF) joins the Mosha Fault (MF) in the north-eastern margin of Tehran and can provide important paleosismological information for Tehran. The Ira trench, were divided into 6 packages (I to VI), described, according to their composition, relative and absolute ages. Package I consists of units 23, 25, 26, 27, 28, 29, 30 and 31. The whole package I mainly belongs to Holocene, and provides essential constraints for the recent paleo-earthquake activity of the EMF and NTF zone. Therefore, finding accurate ages for the units of this package is very important. Three colluvial wedges (units 23, 26, 28) are present between 20 and 36.5 m north in package I, which are assigned to 3 episodes of activity on Fault 13. Central age model (CAM) provided OSL ages of 35.0 ± 6.1, 7.3 ± 1.3, 6.4 ± 0.9 and 56 ± 6.5 ka for units 23, 26, 28 and 29, respectively. The conflicting ages of 56 ± 6.5 and 35.0 ± 6.1 ka (for units 23 and 29, respectively) as compared to the underlying younger units suggest that these ages are overestimated. MAM provided OSL ages of 13.1 ± 4.3 and 3.5 ± 0.4 ka for units 23 and 29, respectively. The contribution of the new statistical age model of sample IRA4 to the paleoseismic data is discussed.

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Identification and assessment of medical errors in the triage area of an educational hospital using the SHERPA technique in Iran

Ghasemi M., Khoshakhlagh A. H., Mahmudi S., Fesharaki M. G.

International Journal of Occupational Safety and Ergonomics

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2015

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Vol. 21, No. 3

382--390

EN

Accidents caused by human error are prominent in the medical field. The present study identified medical errors in the emergency triage area by assessing the tasks of all healthcare workers employed in the triage area of an educational hospital in Tehran, Iran in 2014. Data were collected using the systematic human error reduction and prediction approach (SHERPA). The tasks and sub-tasks were determined and analyzed using hierarchical analysis and the errors were extracted. A total of 199 human errors were identified in the different tasks. The rate of error for action was 46.8%, checking was 25.6%, retrieval was 8.5%, communication was 12.1% and selection was 7%. Rate of unacceptable and unfavorable risks were 21.1% and 38.6%, respectively. SHERPA was shown to be an appropriate technique for detecting medical errors. The establishment of control programs should be a high priority in the management and implementation of health facilities in triage areas.

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An Algorithmic-type Classification of Tetravalent One-regular Graphs Using Computer Algebra Tools

Ghasemi M.

Fundamenta Informaticae

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2014

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Vol. 135, nr 3

211--228

EN

A finite graph is one-regular if its automorphism group acts regularly on the set of its arcs. In the present paper, tetravalent one-regular graphs of order 7p2., where p is a prime, are classified using computer algebra tools.

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Application of SHERPA to Identify and Prevent Human Errors in Control Units of Petrochemical Industry

Ghasemi M., Nasleseraji J., Hoseinabadi S., Zare M.

International Journal of Occupational Safety and Ergonomics

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2013

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Vol. 19, No. 2

203--209

EN

Introduction. Studying human errors as a risk factor in the occurrence of accidents is necessary. Thus, the aim of this study was to identify, predict and control human errors in industrial control units. Method. This is a case study carried out using SHERPA in the first unit of Zagros Methanol of Asalooyeh, Iran, and its subunits. To collect the required data, various methods were used: observing, interviewing processing specialists and control unit operators, and studying technical documents and records. Results. In total, 222 human errors were identified in various occupational tasks. This study showed that 48.62% of them were action errors, 31.97% were checking errors, 6.75% were retrieval errors, 11.70% were communication errors and 0.90% were selection errors. Conclusion. It can be inferred that this method is appropriate for different industries, and it is useful for identifying human errors leading to hazardous accidents.