Showing 3 results for Sadeghian
Soheil Saadat, Iraj Alimohammadi, Mojgan Karbakhsh, Hassan Ashayeri, Farideh Sadeghian, Shahrbanoo Goli, Mahsa Fayaz,
Volume 8, Issue 2 (6-2018)
Abstract
Introduction: Impairment of alertness, attention and performance associated with sleepiness and fatigue in nurses occur in night and long-term shifts that in the end of night shift reach to the maximum level can lead to traffic accidents when they returning home. The purpose of this study was to determine the effect of night shift on psychomotor abilities of driving in nurses after shiftwork.
Material and Method: A cohort study was carried out on 23 night shift and 24 day shift female nurses aged 20 to 40 at Sina Hospital in Tehran city, using the Vienna Test System (VTS). The concentration and selective attention, reaction time, pheriperal perception, and coordination before and after night and day shifts were measured. A multiple linear regression model and Backward stepwise selection method was used for analyses.
Result: In the concentration and selective attention test, sum hits (p = 0.038) and in the visual perception test , divided attention (p =0.006) and visual field (p =0.019), and in the reaction time test the mean motor time (p =0.034) showed a significant adverse relationship with working in night shift, but the visomotor coordination variables did not show any significant correlation.
Conclusion: The results showed that the concentration and selective attention, peripheral perception, and reaction time of psychomotor ability of driving were significantly adversely impaired in nurses after night shift. These results in evidence of the mechanism of increasing traffic accidents after night shift among nurses added to the previous studies in this subject.
Mojtaba Zokaei, Marzieh Sadeghian, Mohsen Falahati, Azam Biabani,
Volume 13, Issue 4 (12-2023)
Abstract
Introduction: Due to the increase in the provision of electronic services to citizens in government offices, the number of computer users and the occurrence of musculoskeletal disorders have increased. Therefore, this study aimed to predict and model the complex relationships between the risk factors of musculoskeletal disorders in computer users working in government offices by an artificial neural network.
Material and Methods: The current cross-sectional study was conducted in 2020 on 342 employees of various government offices in Saveh city. First, the researcher visited the work environment to identify the problems and measure the environmental factors. Then, ergonomic risk assessment and psychosocial factors were evaluated using the Nordic questionnaire and the ROSA method. The effect of various factors in causing musculoskeletal disorders was investigated using a logistic regression test.Then the resulting data were collected and modeled by one of the neural network algorithms. Finally, artificial neural networks presented an optimal model to predict the risk of musculoskeletal disorders.
Results: The results showed that by increasing the level of social interactions, the level of demand, control, and leadership in the job, musculoskeletal disorders in men and women decrease. There was a significant relationship between the prevalence of musculoskeletal disorders and job demand, job control levels, social interaction levels, leadership levels, organizational climate levels, job satisfaction levels, and stress levels, in addition between reports of pain in the neck and shoulder and wrist/hand region. There was a significant relationship with the overall ROSA score. Also, there was a significant relationship between the report of pain or discomfort in the neck area with the phone screen risk score, wrist/hand with the keyboard-mouse risk score, and shoulder, upper back, elbow, and lower back with the chair risk score. The accuracy of the presented model for predicting musculoskeletal disorders was also about 88.5%, which indicates the acceptability of the results.
Conclusion: The results showed that several factors play a role in causing musculoskeletal disorders, which include individual, environmental, psychosocial, and workstation factors. Therefore, in the design of an ergonomic workstation, the effects of the mentioned factors should be investigated. Also, predicting the effectiveness of each of the mentioned factors using an artificial neural network showed that this type of modeling can be used to prevent musculoskeletal disorders or other multifactorial disorders.
Zahra Hashemi, Mohammad Javad Sheikhmozafari, Azma Putra, Marzie Sadeghian, Nasrin Asadi, Saeid Ahmadi, Masoumeh Alidostie,
Volume 14, Issue 3 (10-2024)
Abstract
Introduction: Microperforated panels (MPPs), often considered as potential replacements for fiber absorbers, have a significant limitation in their absorption bandwidth, particularly around the natural frequency. This study aims to address this challenge by focusing on the optimization and modeling of sound absorption in a manufactured MPP.
Material and Methods: The study employed Response Surface Methodology (RSM) with a Central Composite Design (CCD) approach using Design Expert software to determine the average normal absorption coefficient within the frequency range of 125 to 2500 Hz. Numerical simulations using the Finite Element Method (FEM) were conducted to validate the RSM findings. An MPP absorber was then designed, manufactured, and evaluated for its normal absorption coefficient using an impedance tube. Additionally, a theoretical Equivalent Circuit Model (ECM) was utilized to predict the normal absorption coefficient for the manufactured MPP.
Results: The optimization process revealed that setting the hole diameter to 0.3 mm, the percentage of perforation to 2.5%, and the air cavity depth behind the panel to 25 mm resulted in maximum absorption within the specified frequency range. Under these optimized conditions, the average absorption coefficient closely aligned with the predictions generated by RSM across numerical, theoretical, and laboratory assessments, demonstrating a 13.8% improvement compared to non-optimized MPPs.
Conclusion: This study demonstrates the effectiveness of using RSM to optimize the parameters affecting MPP performance. The substantial correlation between the FEM numerical model, ECM theory model, and impedance tube results positions these models as both cost-effective and reliable alternatives to conventional laboratory methods. The consistency of these models with the experimental outcomes validates their potential for practical applications.
