Structural Response of Steel Jacket-UHPC Retrofitted Reinforced Concrete Columns under Blast Loading

The lateral capacity of exterior concrete columns subjected to a blast load is the key factor in the building collapse probability. Due to potentially severe consequences of the collapse, efforts have been made to improve the blast resistance of existing structures. One of the successful approaches is the use of ultra-high-performance-concrete (UHPC) jacketing for retrofitting a building’s columns. The columns on the first floor of a building normally have higher slenderness due to the higher first story. Since an explosion is more likely to take place at the ground level, retrofitting the columns of the lower floors is crucial to improve a building’s blast resistance. Casting a UHPC tube around a circular RC column can increase the moment of inertia of the column and improve the flexural strength. In this study, a retrofitting system consisting of a UHPC layer enclosed by a thin steel jacket is proposed to improve the blast resistance of buildings in service. Most of the previous research is focused on design aspects of blast-resistant columns and retrofitting systems are mostly based on fiber reinforced polymers or steel jackets. A validated FE model is used to investigate the effectiveness of this method. The results showed significant improvement both at the component and building system levels against combined gravity and blast loading.     

Applying Wells score to inconclusive perfusion only modified PIOPED II (Prospective Investigation of Pulmonary Embolism Diagnosis II) readings in order to optimize the lung scintigraphy diagnostic yield in acute pulmonary embolism detection

Annals of Nuclear Medicine - When using perfusion only modified PIOPED II criteria for PE detection, generated non-diagnostic scans are found to be the main diagnostic restriction. The objective of...     

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.