In the present work effect of recycled heating and cooling on the values of concrete compressive strength due to high temperature of 400⁰C was studied.

    The tests show that the percent of reduction in compressive strength of the samples which exposed to a temperature of 400⁰C for one cycle was 32.5%, while the reduction was 52.7% for the samples which were exposed to recycled heating and cooling of ten times .      

   Moreover a study of the effect of specimen sizes on the percentages of compressive strength reduction due to high temperature

Background: The study aimed to investigate the effect of different techniques of en masse retraction on the vertical and sagittal position, axial inclination, rate of space closure, and type of movement of maxillary central incisor. Materials and methods: A typodont simulation system was used (CL II division 2 malocclusion). Three groups were used group 1(N=10, T-loop), group 2(N=10, Time-Saving loop), and group 3(N=10, Microimplant). Photographs were taken before and after retraction and measurements were made using Autodesk AutoCAD© software 2010. Kruskal-Wallis one-way analyses of variance and Mann-Whitney U test (p?0.05) were used. Results: The rate of space closure showed no significant difference among the three groups (p?0.05), whi

Depp and Heard's trial has reaped significant attention due to the domestic violence allegations directed towards each other. This paper sheds light on the repressed narrative beyond the mere words spoken aloud. It delves into an overlooked aspect, i.e., nonverbal communication. Previous studies focused on one or two categories of nonverbal communication. Therefore, the current study investigates the types and sub-types of nonverbal communication exhibited by both rivals within the courtroom setting. To examine the credibility and repressibility of nonverbal communication, the researchers have carefully watched (28) videos representing the whole trial's event. Some nonverbal communication was traced through the whole (28) videos fro

Background: The Epstein-Barr virus (EBV) relates to the torch virus family and is believed to have a substantial impact on mortality and perinatal events, as shown by epidemiological and viral studies. Moreover, there have been documented cases of EBV transmission occurring via the placenta. Nevertheless, the specific location of the EBV infection inside the placenta remains uncertain. Methods: The genomic sequences connected to the latent EBV gene and the levels of lytic EBV gene expression in placental chorionic villous cells are examined in this work. A total of 86 placentas from patients who had miscarriage and 54 placentas from individuals who had successful births were obtained for analysis. Results: The research employed QPCR to dete

Cladosporium sp. plays an important role in human health, it is one of the pathogenic fungi which cause allergy and asthma and most frequently isolated from airborne spores.  In this study, a couple of universal PCR primers were designed to identify the pathogenic fungi Cladosporium sp. according to conserved region 5.8S, 18S and 28S subunit ribosomal RNA gene in Cladosporium species. In silico RFLP-PCR were used to identify twenty-four Cladosporium strains. The results showed that the universal primer has the specificity to amplify the conserved region in 24 species as a band in virtual agarose gel. They also showed that the RFLP method is able to identify three Cladosporium spe

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in