Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(3-caprolactone) (PCL)/gelatin (Gt) nanofibers.Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFM MLP is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving.According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFM MLP in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning.
Background: musculoskeletal disorders are one of the most common occupational diseases in hospital staff. Factors that are effective on the incidence of musculoskeletal disorders can point to stress and job satisfaction and unsuitable postures. By regarding the key role of nursing staff in hospitals in giving health services, this study has been done with the aim of the survey about the relationship between stress amount and job satisfaction with musculoskeletal disorders in nurses. Methods: This analytical description study had been done in the year 2017, accidentally between 140 persons, 90 of available nurses that during sampling were in the nursing station of one of Tehran hospitals. For data collection one used standard questionnaire composed of demographic information and job information about musculoskeletal disorders, satisfaction, and job stress. Also for finding the relationship between variables one used correlation test, multivariate linear regression and independent t-test by SPSS20 Software. Results: Results shows that one of the biggest postures adopted by nurses is waist bending that the result of that is a pain in the lower part of the waist. By doing multivariate linear regression in SPSS20, a model for prediction pain in the lower part results from waist bending gained. Between job stress and job postures, there have been seen a significant relationship (P<0/05) in this test there was no significant relationship between job satisfaction and job postures. Conclusion: Extra physical activities lead to unsuitable and repetitive postures in nurses that are one of the most important factors in creating job stress in nurses. For decreasing coming stress to staff, they should give information and needed knowledge about correct posture adoption to everyone.
Background: Musculoskeletal disorders are one of the most common factors that lead to occupational injuries among hospital staff. Considering the key role of hospital staffs in providing health services to patients, this study was conducted to assess risk factors that are effective on low back pain and the use of adaptive neuro-fuzzy inference system (ANFIS) model to predict it. Methods: This cross-sectional study was conducted in 90 nurses of the Isfahan hospitals in 2018. First, the risk factors that affect pain in the lumbar region was assessed, then a model with the precision of 0.91% to predict low back pain was developed using the ANFIS by the MATLAB2016a software. Results: First, linear regression model showed four risk factors repetitive movements, long-standing, bending of the back, and carrying heavy objects were the most significant ones compared to other risk factors associated with musculoskeletal disorders. After a study of these risk factors in the ANFIS, various tests were conducted and the best model with a confidence level of 91% was selected as the model. Conclusion: The ANFIS can be used as an appropriate tool to predict lower back pain.
The spirometry is considered a preclinical tool for the evaluation of the respiratory system. The formal lung volumes measurement and health status lung system are made using spirometry. Artificial neural network (ANN) has been introduced in solving complex problems in a large number of different settings, including medical diagnosis support system as predictive power. An objective of this research was intended to investigate the development of a new decision support system (DSS) using ANN modeling approaches and algorithms to predict pulmonary function in people. The spirometry data and general characteristics, anthropometric data, and body composition parameters (N = 130) were obtained from subjects. The classification of pulmonary function was performed by the multi-layer perceptron (MLP) model. Findings show that the MLP model is capable of classifying respiratory abnormalities in different people. The ANN model was totally 93.6%, 92.3%, 84.6%, and 91.5% successful in correctly classified in training, validation, test, and all data, respectively. Also, a DSS tool was created that allows the evaluation and classification of the results of spirometry data. It appears that ANNs are useful in classification pulmonary function.
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