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عضویت
فهرست مطالب نویسنده:

z. mohebbi

  • Z. Mohebbi, E. Saeedzadeh, H. Mahani *

    While the conventional parallel-hole collimators are widely used in myocardial perfusion imaging (MPI) with SPECT, they are suboptimal in balancing the existing sensitivity-resolution compromise. Therefore, multi-pinhole collimation has been proposed to address the problem. In the present study, a channel multi-pinhole collimated SPECT scanner is modeled and then simulated using the GATE Monte Carlo simulation. The multi-pinhole collimator comprises eight apertures. The material, diameter, and height of the apertures were assumed to be varying. A comparison with conventional single-pinhole was also conducted. The results show that increasing the hole diameter leads to degraded spatial resolution for the multi-pinhole collimator. Compared to the single-pinhole, the multi-pinhole collimators suffer from projection overlapping and thus deteriorated spatial resolution. The findings confirm that the channel multi-pinhole collimators outperform the single-pinhole apertures by providing much higher sensitivity at the expense of slightly lower spatial resolution and therefore would be the collimator of choice for MPI with SPECT.

    Keywords: SPECT, Monte Carlo, GATE, MPI, Multi-pinhole
  • Z. Mohebbi, F. Sharif, A. Jaberi*, S. Fazel Dehkordi
    Aims

     The general health of nurses is always at risk due to difficult working conditions. Although exercise interventions, especially aerobic exercise, may improve the general health of nurses, no study has been found in this area so far. Therefore, the present study aimed to investigate the impact of aerobic exercise on the general health of nurses.

    Materials & Methods

     This interventional study was carried out on 60 female nurses working in hospitals affiliated with Shahrekord University of Medical Sciences in 2017. Nurses were selected by the available sampling method and assigned to control and intervention groups by block randomization. Data were selected by General health and demographic questionnaires. The intervention group practiced three 1-hour sessions per week for eight weeks in the gym according to the exercise program, while the control group did not receive any intervention. The general health questionnaire was assessed in two groups before, immediately, and two months after the intervention. Data were analyzed using SPSS 19 software through T, chi-square tests, and repeated measures analysis of variance.

    Findings

    The mean±SD age of participants was 33.0±2.7 years. The effect of the group was significant (p=0.001) and showed an improvement in the general health of nurses participating in the intervention group after the intervention compared to the control group (p=0.02). The effect of time was also significant because the mean general health of participants changed significantly over time in both control and intervention groups (p<0.001). However, time-group interaction was not significant (p>0.05).

    Conclusion

     Aerobic exercise in nurses improves general health in the initial period after the intervention.

    Keywords: Nurses, Aerobic Exercise, Health Status
  • Z. Mohebbi, Sh. Najafi Doulatabad, M. Malekzadeh, S.H. Nabavizadeh, H. Marioryad, E. Momeni, M. Niazi, M. Zoladl*
    Aim

    Asthma is one of the common, chronic respiratory diseases which psychological factors play an important role in it and comorbidities of several psychological disorders such as anxiety, depression, and panic attacks are common in asthmatic patients. The aim of this study was to determine the effect of hypnosis on pulmonary function and severity of the disease in asthmatic patients.

    Materials and Methods

    This randomized clinical trial study, was carried out on 64 asthmatic patients, who referred to the clinic of Shahid Mofattah in Yasuj, Iran in 2010. The subjects were selected by purposeful sampling method, and randomly assigned into the tow group of the intervention (subjects were treated by solo hypnosis in a 90-minute session and conventional treatments) and control group (subjects were treated by conventional treatments, in addition, providing an explanation about requirement care point for improvement of asthma in 15 minutes) by use of balanced block randomization. In addition to completing demographic information at the beginning of the study, determination of pulmonary function and asthma severity were conducted respectively through measurement of FEV1 and NAEPP classification (EPR-3) at the starting point of the study and one month after the completion of the research intervention. Data were analyzed using SPSS 21 software, through independent sample t-test, paired sample t-test, Chi-square test, and marginal homogeneity test.

    Findings

    one month after completing the research intervention, the FEV1 in the intervention group increased than the control group and the severity of asthma in the intervention group decreased than the control group.

    Conclusion

    Hypnosis as an adjuvant treatment is effective in the improvement of pulmonary function and reducing the disease severity in asthmatic patients.

    Keywords: Hypnosis, Asthma, Complementary Therapies, Forced Expiratory Volume, Severity Of Illness Index
  • زینب محبی، شهرام صدقی*، مسعود رودباری، جواد غلام نژاد
    مقدمه

    کتابخانه ها و مراکز اطلاع رسانی معمولا با استفاده از ابزار لیب کوآل به بررسی کیفیت خدمات می پردازند. در تجزیه و تحلیل لیب کوآل می توان از شبکه عصبی مصنوعی استفاده نمودکه با درجه خطای پایین به بررسی سطح خدمات می پردازد. پژوهش حاضر در پی آن است که به معرفی شبکه عصبی مصنوعی بپردازد تا در پیش بینی کیفیت خدمات سایر کتابخانه های دانشگاهی مثمر ثمر واقع شود.

    روش کار

    این پژوهش با استفاده از روش پیمایشی و از نوع کاربردی مقطعی می باشد. جامعه پژوهش شامل کلیه دانشجویان دانشگاه علوم پزشکی شیراز است. ابزار گرد آوری داده ها پرسشنامه لیب کوآل می باشد. به منظور تجزیه و تحلیل داده ها و ارائه شبکه عصبی مناسب از نرم افزار MATLAB استفاده شده است. همچنین جهت دستیابی به مناسب ترین شبکه عصبی مصنوعی که قادر به پیش بینی کیفیت خدمات با کمترین خطا و بیشترین میزان تطابق باشد الگوریتمی در نرم افزار MATLAB نوشته شد که به صورت خودکار با گرفتن داده های ورودی و داده های هدف به محاسبه چنین شبکه ای بپردازد.

    یافته ها

    به ازای پنج دسته داده ورودی و اجرای الگوریتم نوشته شده، پنج شبکه عصبی مصنوعی ایجاد گردید که به ترتیب دارای ضریب تعیین 77059/0، 68280/0، 81089/0، 79161/0 و 83273/0 می باشد.

    نتیجه گیری

    با مقایسه شبکه های عصبی مصنوعی مشخص گردید شبکه عصبی مصنوعی که دارای 20 لایه پنهان،80 درصد داده آموزشی، 667/16 درصد داده آزمایشی، 3333/3 درصد داده اعتبارسنجی باشد و به وسیله داده های پنج ورودی تغذیه شود، مناسب ترین شبکه در ارزیابی کیفیت خدمات کتابخانه های دانشگاهی می باشد.

    کلید واژگان: شبکه عصبی، خدمات کتابخانه ای، مدیریت خدمات سازمان ها
    Z. Mohebbi, Sh Sedghi, M. Roudbari, J. Gholamnejad
    Introduction

    Commonly libraries and information centers use LibQual to measure their quality of services. Although analysis of Libqual done with classical statistics، it is possible to analyze it through Artificial Neural Network with lower error rate. This research try to introduce an Artificial Neural Network that is able to predict s ervice quality of university library.

    Methods

    In this applied cross-sectional study، all of Shiraz university of medical science students were assessed. LibQual questionnaire was the instrument of data collection and MATLAB software was being used to analyze data. In addition an algorithm was written to automatic selection of the best network architecture based on lower error rate and higher adaptation rate.

    Results

    for 5 categories of input data and with running of the written algorithm، 5 ANN was created and their matching ratio is 0. 77059، 0. 6828، 0. 81089، 0. 79161 and 0. 83273 respectively.

    Conclusion

    By comparing the ANNs، it was found that ANN with 20 hidden layer، %80 training data، %16. 667 testing data and %3. 3333 validation data that be fed with fifth input data، is the most appropriate ANN in quality evaluation of university libraries.

    Keywords: Neural Networks(Computer), Library Services, Management Service Organizations
سامانه نویسندگان
  • زینت محبی
    زینت محبی

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