Fiber diameter plays an important role in the properties of electrospinning of nanofibers. However, one major problem is the lack of a comprehensive method that can link processing parameters to nanofibers' diameter. The objective of this study is to develope an artificial neural network (ANN) modeling and multiple regression (MLR) analysis approaches to predict the diameter of nanofibers. Processing parameters, including weight ratio, voltage, injection rate, and distance, were considered as independent variables and the nanofiber diameter as the dependent variable of the ANN model. The results of ANN modeling, especially its high accuracy (R 2 = 0.959) in comparison with MLR results (R 2 = 0.564), introduced the prediction the diameter of nanofibers model (PDNFM) as a comparative model for predicting the diameter of poly (3-caprolactone) (PCL)/gelatin (Gt) nanofibers. According to the result of sensitivity analysis of the model, the values of weight ratio, distance, injection rate, and voltage, respectively, were identified as the most significant parameters which influence PDNFM.
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.
Some occupational skin exposures lead to the formation of reactive oxygen species (ROS). The occupational exposure of workers to ROS has been found to be associated with an increased risk of developing skin injuries; therefore, it is essential to protect skin against ROS formation. Recently, some studies have been conducted on introducing better alternatives for skin protection. Nanofibers are good candidates for this purpose. The current study was carried out to assess vitamin E-loaded hybrid Poly(ε-caprolactone) (PCL)/gelatin (Gt) nanofibres mats as protective layers of skin exposed to occupational exposures. Vitamin E (VE) was successfully incorporated into PCL/Gt nanofibers while they were formed by electrospinning method. Nanofibers mats were characterized using scanning electron microscopy (SEM) and fourier transform infrared spectroscopy (FTIR). Their degradation behavior, in vitro release, biocompatibility, and antioxidant activity were studied. The diameters of the PCL/Gt/VE nanofibers decreased with the addition of vitamin E. The degradation rate of nanofibers was equal to 42.98 and 50.69% during 7 and 14 days, respectively. Nanofibers containing vitamin E showed an initial burst followed by a sustained release. The PCL/Gt/VE nanofibers exhibited good free radical scavenging activities despite being exposed to a high electrical potential during electrospinning. PCL/Gt/VE nanofibers supported a higher level of viability compared to PCL/Gt ones and significantly assisted human skin cells against tert-Butyl hydroperoxide (t-BHP) induced oxidative stress. Overall, PCL/Gt/VE nanofibers can potentially be used to protect skin against oxidative stress as a novel approach for worker’s healthcare.
Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.
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