Thermodynamic geometry of a system with unified quantum statistics

We examine the thermodynamic characteristics of unified quantum statistics as a novel framework that undergoes a crossover between Bose-Einstein and Fermi-Dirac statistics by varying a generalization parameter $\delta$. We find an attractive intrinsic statistical interaction when $\delta\le0.5$ where the thermodynamic curvature remains positive throughout the entire physical range. For $0.5 < \delta < 1$ the system exhibits predominantly Fermi-like behavior at high temperatures, while at low temperatures, the thermodynamic curvature is positive and the system behaves like bosons. As the temperature decreases further, the system undergoes a transition into the condensate phase. We also report on a critical fugacity ($z = Z^*$) defined as the point at which the thermodynamic curvature changes sign, i.e. for $z Z^*$), the statistical behavior resembles that of fermions (bosons). Also, we extract the variation of statistical behaviour of the system for different values of generalization parameter with respect to the temperature. We evaluate the critical fugacity and critical $\delta$ dependent condensation temperature of the system. Finally, we investigate the specific heat as a function of temperature and condensation phase transition temperature of the system for different values of generalization parameter in different dimensions.Comment: 10 pages, 17 figure

Panel data analysis in medical research

Introduction: A longitudinal study involves repeated observations of the same items over long periods oftime. Panel studies are longitudinal studies of batch which combine cross-sectional and time- series data inobservations on a number of over time that play special role in medical research or clinical trials. In thispaper, we primarily discuss about the panel data and various modeling of panel data. It is not possible to useordinary regression methods due to inter- correlation of observation related to one unit. Generalizedestimation equation (GEE) for estimation of panel regression coefficient by considering inter correlationwas used among observations.Materials and Methods: We considerd the importance of panel data modeling with GEE in real paneldata samples as applications in the effect of estrogen patches on the postnatal depression.Results: In the estimation of panel regression coefficients on real data, starting treatment effect(b(pre)=0.428, p<0.001), treatment group effect (b(group)=4.025, p<0.001) and treatment period effect(b(visit)=1.218,p<0.001 with use of robust method to misspecification of the correlation structure weresignificant.Conclusion: According to the important role of panel data inter- correlation structure in analyzing inmedical research, we propose to estimate the coefficients of panel via GEE metho

Effects of application municipal effluent on heavy metal (Cr and Ni) accumulation in Olea europaea L. trees and soil

The aim of the study was to determine the accumulation of heavy metals (Cr and Ni) in different layers of soil, leaves and fruits of Olea europaea. In this investigation, irrigation of olive trees were done with both water of well (control) and sewage, for seven years in Rey town, south of Tehran. In each treatment (well water and municipal effluent), three samples were selected systematic randomly. In each sample, leaves and fruits of olive trees and soil (from 0-15, 15-30 and 30-60 cm depths), were collected for analyses in three replications. Samples were analyzed with standard methods and used atomic absorption based on flame (PU9400X). For comparison of concentrations of heavy metals in layers of soil, leaves and fruits and in order to normalizing of data, independent sample t-test was used. Results of this study show that irrigation with municipal effluent increase concentration of Ni and Cr in soil. Concentration of Ni and Cr were statistically greater in leaves of trees irrigated with municipal effluent than those of the leaves of trees irrigated with well water. There was no significant difference in spite of accumulation of heavy metals (Ni and Cr) in Olivefruits

Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy

Purpose: Absorbed dose calculation in magnetic resonance-guided radiation therapy (MRgRT) is commonly based on pseudo CT (pCT) images. This study investigated the feasibility of unsupervised pCT generation from MRI using a cycle generative adversarial network (CycleGAN) and a heterogenous multicentric dataset. A dosimetric analysis in three-dimensional conformal radiotherapy (3DCRT) planning was also performed.Material and methods: Overall, 87 T1-weighted and 102 T2-weighted MR images alongside with their corresponding computed tomography (CT) images of brain cancer patients from multiple centers were used. Initially, images underwent a number of preprocessing steps, including rigid registration, novel CT Masker, N4 bias field correction, resampling, resizing, and rescaling. To overcome the gradient vanishing problem, residual blocks and mean squared error (MSE) loss function were utilized in the generator and in both networks (generator and discriminator), respectively. The CycleGAN was trained and validated using 70 T1 and 80 T2 randomly selected patients in an unsupervised manner. The remaining patients were used as a holdout test set to report final evaluation metrics. The generated pCTs were validated in the context of 3DCRT.Results: The CycleGAN model using masked T2 images achieved better performance with a mean absolute error (MAE) of 61.87 ± 22.58 HU, peak signal to noise ratio (PSNR) of 27.05 ± 2.25 (dB), and structural similarity index metric (SSIM) of 0.84 ± 0.05 on the test dataset. T1-weighted MR images used for dosimetric assessment revealed a gamma index of 3%, 3 mm, 2%, 2 mm and 1%, 1 mm with acceptance criteria of 98.96% ± 1.1%, 95% ± 3.68%, 90.1% ± 6.05%, respectively. The DVH differences between CTs and pCTs were within 2%.Conclusions: A promising pCT generation model capable of handling heterogenous multicenteric datasets was proposed. All MR sequences performed competitively with no significant difference in pCT generation. The proposed CT Masker proved promising in improving the model accuracy and robustness. There was no significant difference between using T1-weighted and T2-weighted MR images for pCT generation.</p