arXiv subjects

Behnam Ashrafkhani

Publications and source records attributed to Behnam Ashrafkhani.

3 recordsLinked to original sources

arXiv2024

Using laser and ultrasound devices for breast cancer treatment: studying the effects of gold nanoparticles injection

Breast cancer is a disease in which cells in the breast grow out of control. After surgery, chemotherapy, and other invasive treatments, hyperthermia is a suitable choice with the minimal side effects. In this paper, the treatment of breast cancer using a combination of laser and ultrasound irradiation, in the presence of gold nanoparticles, is investigated. In the simulations, the breast tissue is represented as a multilayer structure and the tumor is supposed to consist two parts: a superficial section and a deeper region. In the initial stage, the superficial parts of the tumor, which also contain gold nanoparticles, are exposed to a continuous laser for 50 seconds followed by a cooling period of 20 seconds. Then, for deeper sections, ultrasound irradiation is utilized. The results indicated that the tissue necrosis volume was enhanced by the application of nanoparticles. Furthermore, it was demonstrated that the combinational application of both of laser and ultrasound irradiation could eradicate both the superficial parts of the tumor and the deep parts.

physics.med-ph↗

arXiv2023

Direct mass measurements of neutron-rich zinc and gallium isotopes: an investigation of the formation of the first r-process peak

The prediction of isotopic abundances resulting from the rapid neutron capture process (r-process) requires high-precision mass measurements. Using TITAN's on-line time-of-flight spectrometer, first time mass measurements are performed for $^{83}$Zn and $^{86}$Ga. These measurements reduced uncertainties, and are used to calculate isotopic abundances near the first r-process abundance peak using astrophysical conditions present during a binary neutron star (BNS) merger. Good agreement in abundance across a range of trajectories is found when comparing to several metal-poor stars while also strongly deviating from the solar r-process pattern. These findings point to a high degree of sensitivity to the electron fraction of a BNS merger on the final elemental abundance pattern for certain elements near the first r-process peak while others display universality. We find that small changes in electron fraction can produce distinct abundance patterns that match those of metal-poor stars with different classifications.

nucl-ex↗

arXiv2023

A comparison between Recurrent Neural Networks and classical machine learning approaches In Laser induced breakdown spectroscopy

Recurrent Neural Networks are classes of Artificial Neural Networks that establish connections between different nodes form a directed or undirected graph for temporal dynamical analysis. In this research, the laser induced breakdown spectroscopy (LIBS) technique is used for quantitative analysis of aluminum alloys by different Recurrent Neural Network (RNN) architecture. The fundamental harmonic (1064 nm) of a nanosecond Nd:YAG laser pulse is employed to generate the LIBS plasma for the prediction of constituent concentrations of the aluminum standard samples. Here, Recurrent Neural Networks based on different networks, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (Simple RNN), and as well as Recurrent Convolutional Networks comprising of Conv-SimpleRNN, Conv-LSTM and Conv-GRU are utilized for concentration prediction. Then a comparison is performed among prediction by classical machine learning methods of support vector regressor (SVR), the Multi Layer Perceptron (MLP), Decision Tree algorithm, Gradient Boosting Regression (GBR), Random Forest Regression (RFR), Linear Regression, and k-Nearest Neighbor (KNN) algorithm. Results showed that the machine learning tools based on Convolutional Recurrent Networks had the best efficiencies in prediction of the most of the elements among other multivariate methods.

cs.LG↗