Jordan University of Science and Technology

About: Jordan University of Science and Technology is a education organization based out in Irbid, Irbid, Jordan. It is known for research contribution in the topics: Population & Medicine. The organization has 7582 authors who have published 13166 publications receiving 298158 citations. The organization is also known as: JUST.

Effects of endodontic post surface treatment, dentin conditioning, and artificial aging on the retention of glass fiber-reinforced composite resin posts

Abstract: Statement of problem Several post surface treatments with or without the application of a bonding agent have been recommended to improve the bond strength of resin cements to posts. A regimen that produces the maximum bond strength of glass fiber-reinforced composite resin posts has not been verified. Purpose The purpose of this study was to evaluate the influence of post surface conditioning methods and artificial aging on the retention and microleakage of adhesively luted glass fiber-reinforced composite resin posts. Material and methods Seventy-two endodontically treated single-rooted teeth were prepared for glass fiber-reinforced composite resin posts. The posts were submitted to 3 different surface treatments (n=24), including no treatment, etching with phosphoric acid, and airborne-particle abrasion. Subgroups of the posts (n=8) were then allocated for 3 different experimental conditions: no artificial aging, no bonding agent; no artificial aging, bonding agent; or artificial aging, bonding agent. The posts were luted with resin cement (Calibra). Post retention was measured in tension at a crosshead speed of 2 mm/min. The posts assigned for microleakage investigation were placed in fuchsin dye for 72 hours. The dislodged posts and the post spaces were examined microscopically to evaluate the mode of failure and explore the microleakage. Data were analyzed by 2-way ANOVA followed by Tukey HSD test (α=.05). Results The mean (SD) retention values for test groups ranged from 269 (63.8) to 349 (52.2) N. The retention values of the airborne-particle-abrasion group were significantly higher than those of the acidic-treatment and no-treatment groups. The application of bonding agent on the post surface produced no significant influence on retention. The mean retention values after artificial aging were significantly higher than without artificial aging. Microscopic evaluation demonstrated that the failure mode was primarily mixed. Conclusions Treating the surface of the posts with phosphoric acid for 15 seconds before cementation produced no significant improvement in post retention. Airborne-particle abrasion of the surface of the post significantly improved post retention.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

Abstract: Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wavelets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight

Abstract: From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions.

Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks.

Abstract: In wireless sensor networks, the energy source is limited to the capacity of the sensor node's battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms' energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.