Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

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(ε-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.

A new tool for prediction the diameter of nanofibers is presented: the use of adaptive modeling techniques to predict fiber diameter and study the impact of electrospinning process parameters on electrospinning fiber diameter.     

MTHFR gene polymorphisms and susceptibility to rheumatoid arthritis: a meta-analysis based on 16 studies

Clinical Rheumatology - Rheumatoid arthritis (RA) is the most common autoimmune rheumatic disease, in which an epigenetic implication in the disease etiopathogenesis has been noted. Here in this...     

Vitamin D receptor (VDR) gene polymorphism and risk of rheumatoid arthritis (RA): systematic review and meta-analysis

Clinical Rheumatology - Vitamin D is involved in immune system modulation as well as in calcium and bone homeostasis, hence plays a role in rheumatoid arthritis (RA) etiopathogenesis. A bulk of...     

The effects simultaneous inhibition of dipeptidyl peptidase-4 and P2X7 purinoceptors in an in vivo Parkinson’s disease model

Metabolic Brain Disease - Loss of dopaminergic neurons following Parkinson’s disease (PD) diminishes quality of life in patients. The present study was carried out to investigate the...     

Mental and personality disorders in infertile women with polycystic ovary: a case-control study

BackgroundPolycystic Ovarian Syndrome (PCOS) is one of the most common causes of infertility in women.ObjectiveThe current study investigated mental and personality disorders in infertile women with and without PCOS.MethodsThis case-control study evaluated 400 infertile women who referred to the Infertility Center in Babol city (North of Iran). Participants were categorized into the case group (201 PCOS) and the control group (199 without PCOS). All of the participants completed the Millon Clinical Multi-axial Inventory-III (MCMI-III).ResultsThe mean scores for clinical personality patterns were significantly higher for six personality disorders (schizoid, avoidant, antisocial, depressive, sadistic, and negativistic) and for three classes of severe personality disorder patterns (schizotypal, borderline, and paranoid) in infertile women with PCOS than in women without PCOS. The mean scores for eight clinical disorders (somatoform, manic disorder, dysthymia, alcohol-dependence, drug-dependence, post-trauma stress disorder, major depression, and delusion disorder) were also higher in infertile women with PCOS than in women without PCOS.ConclusionThe scores of many mental and personality disorders are higher in infertile women with PCOS than in women without PCOS. Thus, clinicians should prioritize recognizing and treating psychological problems of infertile women with PCOS.     

An FBG magnetic sensor for oil flow monitoring in sandstone core

Monitoring the oil movement using a non-contact optical fiber probe during enhanced oil recovery is a novel technique to increase the efficiency of the process by distinguishing the oil position in the reservoir. A partially unclad fiber Bragg grating (FBG) coated with Fe₃O₄ nanoparticles as a magnetic field sensor is experimentally demonstrated. A series of six FBGs reflecting different wavelengths are fixed on the surface of sandstone. Nanofluids containing magnetite nanoparticles and alkaline-surfactant-polymer are injected continuously in two separate steps into the sandstone, which is saturated with 20% oil and 80% brine. The chamber is equipped with a solenoid that acts as a magnetic field generator. The changes in the magnetic field strength depended on the FBG-solenoid distance and the density of localized injected nanoparticles near the FBGs leads to a shift of the reflected wavelength of each single FBG accordingly. The shift is caused by the interference of different propagating modes reflected from the core-cladding and cladding-magnetite layer interfaces. The intensity of the FBG spectra decreases by injecting the nanofluid and vice versa for surfactant injection. The sensor response time of ∼21 s confirms the high reliability and repeatability of the sensing scheme. Movement of oil along the sandstone alters the wavelength shift in the FBG spectra.

Monitoring the oil movement using a non-contact optical fiber probe during enhanced oil recovery is a novel technique to increase the efficiency of the process by distinguishing the oil position in the reservoir.