Intracytoplasmic sperm injection (ICSI) is one of the most successful techniques of Assisted Reproductive Technology (ART) and is mostly in use for the treatment of infertility with male factors. In this method, before injecting sperm into the intracytoplasmic of the oocyte, cumulus cells around the oocyte must be stripped to facilitate the injection process. To achieve this, both enzymatic and mechanical methods are used in embryological LABoratories for denudation, which has major deficiencies, including the possibility of damaging the oocyte prior to the injection process. In this research, a microfluidic-based device is introduced for the separation of cumulus cells around the oocyte with minimum manual operations. The results prove high efficiency, and non-destructive denudation of the oocyte with the reduced amount of culture medium leads to the low-cost preparation process of oocytes. The process can also be integrated with ICSI chips under development and will be reported shortly.

Introduction: The study's purpose was to compare the maxillary All-on-four, All-on-five, and All-on-six designs in terms of stress distribution in the bone and various components of the prosthesis design.Materials and Methods: This finite element analysis study was carried out descriptively in 2024 at the Faculty of Dentistry in Isfahan. An edentulous maxillary model and three implant-based fixed prostheses were designed. Design 1 (All on four): two vertical anterior implants in the position of the lateral teeth and two posterior implants with an angle of 30 degrees in the position of the second premolars. Design 2(All on five): one vertical implant in the position of the central tooth, two vertical implants in the position of the lateral teeth, and two posterior implants with an angle of 30 degrees in the position of the second premolars. Design 3 (All on six): two vertical implants in the position of the central teeth, two vertical implants in the position of the canine teeth, and two short vertical implants in the position of the first molars. A force of 100 Newtons was applied to the left end of the superstructure, and then the maximum stress in the bone and corresponding prosthetic structures were compared.Results: The All-on-Six design caused the least stress in the bone and prosthetic structures, and the All-on-Four design caused the most stress.Conclusion: Among the designs evaluated, the All-on-six demonstrated the most favorable biomechanical performance. The All-on-four design exhibited the highest stress levels within bone and prosthetic components. While the All-on-five design presented intermediate stress levels compared to the other two models.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Facilities built in areas affected by earthquake activity, such as tunnels, which have always been an integral part of human life, must withstand both dynamic and static loading. It has led to the need for practical studies on the effects of earthquakes on underground structures and the factors affecting their destruction. For this purpose, in this research, at first different patterns of tunnel’s excavation were investigated and by using Plaxis 2D software and based on Tabas earthquake in Iran, sensitivity analysis on geotechnical parameters of the soil surrounding tunnel such as cohesion, friction angle, unit weight and modulus of elasticity was carried out, and the parameters whose changes have the greatest and least effects on the bending moment changes on the tunnel lining are introduced. The results show that tunnel excavation patterns significantly affect the bending moment, axial forces, displacements, and surface settlement of the tunnel. Often, by dividing tunnel excavation area to small parts, the values of bending moment, axial forces, displacements, and surface settlement of the tunnel decreases in static analysis. Also, outputs of sensitivity analysis on geotechnical parameters of the soil surrounding tunnel showed that modulus of elasticity of the soil surrounding tunnel has the most effect and cohesion changes have the least effect on bending moment induced on tunnel lining..

The purpose of the present study was to investigate the effect of generative learning teaching based to pattern cognitive Load on the memorization math of 7th grade students of in the academic year of 2021- 2022. The research method is quasi-experimental with pre-test and post-test with four groups: two control group and two experimental groups. The statistical population of the study consisted of all 7th grade male and female students of District 2 of Mashhad city that 112 person sample was selected by single stage cluster sampling method. The subjects in the experimental group involved in generative learning based to cognitive Load in 16 sessions and the control group did not receive any interventions. The research instrument consisted of a 20-questions researcher-made test. To analyze the obtained data, Multivariate analysis of covariance and SPSS version 24 software were used. The results showed that generative learning teaching based to approach cognitive Load have a positive and significant effect on 7th grade students' memorization math. Based on the findings, it can be concluded that one of the effective training on math memorization students is the use of generative learning on the based load cognitive pattern.