A pilot study of diabetes effects on radiation attenuation characteristics of tibia and femur of rats.

This study aimed to investigate the effect of diabetes on radiation attenuation parameters of the femur and tibia of rats using Monte Carlo Simulations. First, control and diabetic rats were identified and tibias and femurs were removed. Then, the elemental ratios of the bones obtained were calculated using EDS (Energy Dissipative X-ray Spectroscopy). Therefore, radiation permeability properties of control and diabetic bones were simulated by using the content ratios in the bones in MCNP6 (Monte Carlo N-Particle) and PHITS (Particle and Heavy Ion Transport code System) 3.22 and Stopping and Range of Ions in Matter (SRIM) simulation codes. Attenuation coefficient results were compared with the NIST database via XCOM. Although differences in absorption coefficients are observed at low energies, these differences disappear as the energy increases.

Neural network predictions of (α,n) reaction cross sections at 18.5±3 MeV using the Levenberg-Marquardt algorithm.

In recent developments, artificial neural networks (ANNs) have demonstrated their capability to predict reaction cross-sections based on experimental data. Specifically, for predicting (α,n) reaction cross-sections, we meticulously fine-tuned the neural network's performance by optimizing its parameters through the Levenberg-Marquardt algorithm. The effectiveness of this approach is corroborated by notable correlation coefficients; an R-value of 0.90928 for overall correlation, 0.98194 for validation, 0.99981 for testing, and 0.94116 for the comprehensive network prediction. We conducted a rigorous comparison between the results and theoretical computations derived from the TALYS 1.95 nuclear code to validate the predictive accuracy. The mean square error value for artificial neural network results is 7620.92, whereas for TALYS 1.95 calculations, it has been found to be 50,312.74. This comprehensive evaluation process validates the reliability of the ANN based on the Levenberg-Marquardt algorithm in approximating the reaction sections, thus demonstrating its potential for comprehensive investigations. These recent developments confirm the feasibility of using ANN models to gain insight into (α,n) reaction cross-sections.

Application of diffuse optical back reflection spectroscopy for determining articular cartilage thickness in a clinical setting.

PURPOSE: Develop a spectroscopic method to assess cartilage thickness during the arthroscopic examination. METHODS: Currently, arthroscopy assesses cartilage damage visually; outcomes are based on the surgeon's subjective experience. Light reflection spectroscopy is a promising method for measuring cartilage thickness based on the absorption of light by the subchondral bone. In the presented study, in vivo diffuse optical back reflection spectroscopic measurements were acquired by gently placing an optical fibre probe on different locations of the articular cartilage of 50 patients during complete knee replacement surgery. The optical fibre probe consists of two optical fibers with a diameter of 1 mm to deliver the light and detect back-reflected light from the cartilage. Centre to centre distance between the source and the detector fibers was 2.4 mm. Actual thicknesses of the articular cartilage samples were measured under microscopy using histopathological staining. RESULTS: Using half of the samples in the patient data, a linear regression model was formed to estimate cartilage thicknesses from the spectroscopic measurements. The regression model was then used to predict the cartilage thickness in the second half of the data. The cartilage thickness was predicted with a mean error of 8.7% if the actual thickness was less than 2.5 mm (R2 = 0.97). CONCLUSION: The outer diameter of the optical fibre probe was 3 mm, which can fit into the arthroscopy channel and can be used to measure the cartilage thickness in real-time during the arthroscopic examination of the articular cartilage.

Estimations for (n,α) reaction cross sections at around 14.5MeV using Levenberg-Marquardt algorithm-based artificial neural network.

Prediction of neutron-induced reaction cross-sections at around the 14.5 MeV neutron energy is crucial to calculate nuclear transmutation rates, nuclear heating, and radiation damage from gas formation in fusion reactor technology In this research, the new approach of (n,α) reaction cross-section is presented. It has been assessed by utilizing the artificial neural network (ANN) when compared to more advanced algorithms, the Levenberg-Marquardt algorithm-based ANN can be exceedingly fast. The correlation coefficients for a training R-value of 0.99283, a validation R-value of 0.991190, a testing R-value of 0.97337, and an overall R-value of 0.98515 demonstrate that Levenberg-Marquardt algorithm-based ANN is well suited for this purpose. . The obtained results were compared to theoretical calculations of TALYS 1.95 nuclear code. As a consequence, it has been demonstrated that the ANN model can be used to determine the systemic study for (n, α) reaction cross-sections.

The detection of cervical neoplasia via optical imaging: a pilot clinical study.

PURPOSE: The present study aims to develop a new high-resolution imaging system for the early diagnosis of cervical neoplasia based on increased vessel density of the cervical tissue. METHODS: An optical device was developed to obtain high contrast and resolution images of vascular structures of the cervix in the present study. The device utilizes a telecentric lens to capture cervix images under light illumination with a wavelength of 550 nm emitted from LEDs. Images were obtained using the telecentric lens with or without acetic acid application to the cervix. Image processing algorithms were used to contrast and extract the skeleton of the vascular structures on the cervix. In the evaluation of the vascular density, the cervical images were divided into 12 o'clock positions, and the fractal dimension of the vascularity was calculated for each dial area between the o'clock positions. The region with the largest fractal dimension was accepted as the region with the highest probability of lesion. The range of vessel sizes was split into small classes of "bins" for each dial area with the highest fractal dimension. To validate the system's success in differentiating between normal and HSIL lesions, forty five patients who underwent colposcopy and biopsy were included in a pilot study. RESULTS: The system correctly classified four HSIL cases out of five and failed to detect one HSIL case, achieving an accuracy rate of 97.8% with an 80% sensitivity and 100% specificity. CONCLUSION: The developed high-resolution optical imaging system may potentially be used in detecting cervical neoplasia just before the biopsy and reduce the number of false-positive cases.

Estimations of level density parameters by using artificial neural network for phenomenological level density models.

The main aim of this study is to develop accurate artificial neural network (ANN) algorithms to estimate level density parameters. An efficient Bayesian-based algorithm is presented for classification algorithms. Unknown model parameters are estimated using the observed data, from which the Bayesian-based algorithm is predicted. This paper focuses on the Bayesian method for parameter estimations of Gilbert Cameron Model (GCM), Back Shifted Fermi Gas Model (BSFGM) and Generalised Super Fluid Model (GSM), which are known as the phonemological level density models. Obtained level density parameters have been compared with the Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations (RIPL) data. R values of the Bayesian method have been found as 0.9946, 0.9981 and 0.9824 for BSFGM, GCM and GSM, respectively. In order to validate our results, default level density parameters of TALYS 1.95 code have been changed with our newly obtained results and photo-neutron cross-section calculations of the 117Sn(γ,n)116Sn, 118Sn(γ,n)117Sn, 119Sn(γ,n)118Sn and 120Sn(γ,n)119Sn reactions have been calculated by using these newly obtained level density parameters.