Objective: Evaluate the effects of different storage periods on flexural strength (FS) and degree of conversion (DC) of Bis-Acryl composite and Urethane dimethacrylate provisional restorative materials. Material and Methods: A total of 60 specimens were prepared from four temporary crown materials commercially available and assigned to four tested groups (n = 15 for each group): Prevision Temp, B&E CROWN, Primma Art, and Charm Temp groups. The specimens were stored in artificial saliva, and the FS was tested after 24 h, 7 d, and 14 d. A standard three-point bending test was conducted using a universal testing machine. Additionally, the DC was determined using a Fourier transform infrared spectroscopy (FTIR) device. The data were analyzed statistically using two- way ANOVA, Tukey`s HSD post-hoc test, and the Bonferroni test, all at a 5% significance level. For each group, a paired samples test was applied to compare the DC of the immediate and 24 h samples. Results: The highest FS value was found for the Prevision Temp material, while the Charm Temp material showed the lowest FS, with no statistically significant difference between the mean values of the groups at 24 h; while there were significant differences at 7d and 14 d of storage. However, within each group, the aging had no significant impact on the FS, except for an increase in the FS of the B&E CROWN group after 14 d. Prevision Temp also had the highest mean DC value. At each time interval, significant differences were recorded. Moreover, within each group of material, aging significantly increased the DC, except for the Primma Art. Conclusion: Bis-acryl composite resin materials exhibited higher flexural strength compared to traditional methyl methacrylate resin during the 14 d investigation period. Aging in artificial saliva did not significantly affect the mechanical performance of the tested materials. Materials with higher DC values showed greater flexural strength; where the Prevision Temp showed higher FS and DC values than the other tested materials.
Mental disorders (MDs) are a common problem in Primary Health Care Centers (PHCCs). Many people with serious MDs are challenged by symptoms and disabilities that result from the disease and by stereotypes and prejudice due to misconceptions about mental illness. This study aims at evaluating the knowledge, and attitude toward mental health concepts and services and causes of the reluctance to seek those services among people attending PHCCs. A descriptive cross-sectional study was conducted. The random sampling technique was used to include (10) of Directorates of Health (DoHs) coverage north, middle, and south of Iraq. The study was executed in (50) selected PHCs, (5) PHCCs in each DoH involved randomly selected (30) people attending th
... Show MoreIn this work various correlation methods were employed to investigate the annual cross-correlation patterns among three different ionospheric parameters: Optimum Working Frequency (OWF), Highest Probable Frequency (HPF), and Best Usable Frequency (BUF). The annual predicted dataset for these parameters were generated using VOCAP and ASASPS models based on the monthly Sunspot Numbers (SSN) during two years of solar cycle 24, minimum 2009 and maximum 2014. The investigation was conducted for Thirty-two different transmitter/receiver stations distributed over Middle East. The locations were selected based on the geodesic parameters which were calculated for different path lengths (500, 1000, 1500, and 2000) km and bearings (N, NE, E, S
... Show MoreAutoría: Muwafaq Obayes Khudhair. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 6, 2022. Artículo de Revista en Dialnet.
In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare
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