This research examines the use of vibratory treatments to reduce residual stresses in small welded parts. In this experimental investigation, a post weld vibration treatment was applied to T- A106 steel pipe fitting specimens to study the effect of the treatment on the residual stress and the hardness of the material. The vibratory stress relief treatment was carried out at different vibration frequency. The results have demonstrated that post-weld vibratory stress relief of small size fittings is possible and residual stress may be relieved, and the treatment may be an alternative method for heat treatment especially when unchange in dimensions and material stability are required.

In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

The study is designed to evaluate the effect of the aqueous extract of the P. lanceolata plant, as well as to know the effect of the drug CCl4 on the formation of micronucleus in vivo 48 female albino mice. In the study mice were separated into eight groups treated intraperitoneally for seven day first group Negative control, second positive control( CCl4 0.02%), third group aqueous extract (250 mg/kg), fourth group  aqueous extract (500 mg/kg), fifth group (CCl4 0.02%) plus aqueous extract (250 mg/kg), sixth group (CCl4 0.02%) plus aqueous extract (500 mg/kg), seventh group aqueous extract (250 mg/kg) plus (CCl4 0.02%), and eighth group aqueous extract (500 mg/kg) plus (CCl4 0.02%). The genetic-cellular aspect involved measuring t

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

LK Abood, RA Ali, M Maliki, International Journal of Science and Research, 2015 - Cited by 2

Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as a mechanism and encompasses